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    "# Exercise 2: Markov Chains and Markov Decision Processes (MDP) \n",
    "\n",
    "This exercise deals with the formal handling of Markov chains and Markov decision processes. \n",
    "\n",
    "## 1) Markov Chain: State Transition\n",
    "The graph shows the last beer problem. \n",
    "The nodes show the states.\n",
    "The arrows define the possible transitions to other states and the numbers besides the arrows define the propability of the corresponding transition.\n",
    "If you are for example in the state \"Inital Beer\", with 30% propability you go to have a pizza, with 60% propability you meet friends and with 10% propability you end up sleeping.\n",
    "\n",
    "Define the state transition probability matrix $\\mathcal{P}_{xx'}$ of the graph shown in the figure below!\n",
    "\n",
    "![](Last_Beer_Graph.png)\n",
    "\n",
    "With $p_k = \\begin{bmatrix}\n",
    "\\text{Pr}_k \\lbrace \\text{Inital Beer} \\rbrace \\\\\n",
    "\\text{Pr}_k \\lbrace \\text{Meet Friends} \\rbrace \\\\\n",
    "\\text{Pr}_k \\lbrace \\text{Pizza} \\rbrace \\\\\n",
    "\\text{Pr}_k \\lbrace \\text{Another Beer} \\rbrace \\\\\n",
    "\\text{Pr}_k \\lbrace \\text{\"Last Beer\"}\\rbrace \\\\\n",
    "\\text{Pr}_k \\lbrace \\text{Sleep} \\rbrace \\\\\n",
    "\\end{bmatrix}^\\text{T}$"
   ]
  },
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   "source": [
    "## 1) Solution \n",
    "\n",
    "The state transition probability $\\mathcal{P}_{xx'} = \\begin{bmatrix}\n",
    "0 & 0.6 & 0.3 & 0 & 0 & 0.1 \\\\\n",
    "0 & 0 & 0.4 & 0.4 & 0.1 & 0.1 \\\\\n",
    "0 & 0 & 0 & 0.7 & 0 & 0.3 \\\\\n",
    "0 & 0 & 0 & 0.6 & 0.3 & 0.1 \\\\\n",
    "0 & 0 & 0 & 0 & 0 & 1 \\\\\n",
    "0 & 0 & 0 & 0 & 0 & 1\n",
    "\\end{bmatrix}$ \n",
    "\n"
   ]
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   "source": [
    "## 2 ) Markov Chain: Stationary State\n",
    "Using $p = p \\mathcal{P}$, calculate the stationary state probability.\n",
    "\n",
    "Please note that the sum of the state propabilities equals one for any specific point in time."
   ]
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    "## 2) Solution\n",
    "\n",
    "\\begin{align}\n",
    "p &= p \\mathcal{P} \\\\\n",
    "p \\left( I_\\text{6} - \\mathcal{P}_{xx'} \\right) &= 0\n",
    "\\end{align}\n",
    "\n",
    "With:\n",
    "\n",
    "$\\left( I_\\text{6} - \\mathcal{P}_{xx'} \\right) = \\begin{bmatrix}\n",
    "1 & -0.6 & -0.3 & 0 & 0 & -0.1 \\\\\n",
    "0 & 1 & -0.4 & -0.4 & -0.1 & -0.1 \\\\\n",
    "0 & 0 & 1 & -0.7 & 0 & -0.3 \\\\\n",
    "0 & 0 & 0 & 0.4 & -0.3 & -0.1 \\\\\n",
    "0 & 0 & 0 & 0 & 1 & -1 \\\\\n",
    "0 & 0 & 0 & 0 & 0 & 0\n",
    "\\end{bmatrix}$ \n",
    "\n",
    "Solving for $p$ yields, that $p_{1,...,5} = 0$.\n",
    "\n",
    "Due to $\\sum_n p_n = 1$: $p_6 = 1$\n",
    "\n",
    "### What does this mean?\n",
    "\n",
    "In the end we all go to sleep, makes sense.\n",
    "\n"
   ]
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   "source": [
    "## 3) Markov Reward Process: Evaluating States\n",
    "\n",
    "In the following rewards for every state are defined.\n",
    "\n",
    "Given the reward distribution $r_\\mathcal{X}$, calculate the state-values $v_\\mathcal{X}$.  \n",
    "\n",
    "The states are defined by:\n",
    "$\\mathcal{X} = \\left\\lbrace \\begin{matrix}\n",
    "\\text{Inital Beer}\\\\\n",
    "\\text{Meet Friends}\\\\\n",
    "\\text{Pizza}\\\\\n",
    "\\text{Another Beer}\\\\\n",
    "\\text{\"Last Beer\"}\\\\\n",
    "\\text{Sleep}\\\\\n",
    "\\end{matrix}\n",
    "\\right\\rbrace$\n",
    "\n",
    "The rewards are defined by:\n",
    "$r_\\mathcal{X} = \\begin{bmatrix}\n",
    "+1\\\\\n",
    "+1\\\\\n",
    "+2\\\\\n",
    "+1\\\\\n",
    "-3\\\\\n",
    "0\\\\\n",
    "\\end{bmatrix}$\n",
    "\n",
    "The state-value is defined by the state-value Bellman equation: $v_\\mathcal{X} = r_\\mathcal{X} + \\gamma \\mathcal{P}_{xx'} v_\\mathcal{X}$. Assume that $\\gamma = 0.9$ and write a Python program to calculate $v_\\mathcal{X}$. Which state is most promising? Why?\n",
    "\n",
    "Which state is most promising when $\\gamma = 0.1$?"
   ]
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   "source": [
    "## 3) Solution\n",
    "\n",
    "\\begin{align}\n",
    "v_\\mathcal{X} &= r_\\mathcal{X} + \\gamma \\mathcal{P}_{xx'} v_\\mathcal{X}\\\\\n",
    "v_\\mathcal{X} - \\gamma \\mathcal{P}_{xx'} v_\\mathcal{X} &= r_\\mathcal{X}\\\\\n",
    "\\left( I_6 - \\gamma \\mathcal{P}_{xx'} \\right) v_\\mathcal{X} &= r_\\mathcal{X}\\\\\n",
    "v_\\mathcal{X} &= \\left( I_6 - \\gamma \\mathcal{P}_{xx'} \\right)^{-1} r_\\mathcal{X}\\\\\n",
    "\\end{align}\n",
    "\n",
    "For $\\gamma=0.9$, the state \"Initial Beer\" has the highest state-value because the highest collectable reward can be expected for the paths one can take from there.\n",
    "\n",
    "For $\\gamma=0.1$, the future collectable reward has lost importance for the evaluation of the state-value, the state-value now gives a rather short-sighted estimation. Thus, the most valuable state can now be found to be the \"Pizza\"-state, as this state holds the highest immediate reward."
   ]
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      "[[ 1.12751902]\n",
      " [ 1.09143404]\n",
      " [ 2.06776596]\n",
      " [ 0.96808511]\n",
      " [-3.        ]\n",
      " [ 0.        ]]\n"
     ]
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   "source": [
    "import numpy as np\n",
    "\n",
    "# define given parameters\n",
    "gamma = 0.1 # discount factor\n",
    "\n",
    "### BEGIN SOLUTION\n",
    "\n",
    "P_xx = np.array([[0, 0.6, 0.3, 0,   0,   0.1],\n",
    "                 [0, 0,   0.4, 0.4, 0.1, 0.1],\n",
    "                 [0, 0,   0,   0.7, 0,   0.3],\n",
    "                 [0, 0,   0,   0.6, 0.3, 0.1],\n",
    "                 [0, 0,   0,   0,   0,   1],\n",
    "                 [0, 0,   0,   0,   0,   1]]) # state trasition probability\n",
    "\n",
    "r_X = np.array([1, 1, 2, 1, -3 ,0]) # rewards\n",
    "r_X = r_X.reshape(-1, 1) # make column vector\n",
    "\n",
    "v_X = np.matmul(np.linalg.inv(np.eye(6)-gamma*P_xx) , r_X)\n",
    "\n",
    "### END SOLUTION\n",
    "\n",
    "print(v_X)\n"
   ]
  },
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   "source": [
    "## 4) Markov Decision Process: State Transition\n",
    "\n",
    "The graph shows an MDP.\n",
    "The nodes are the states. \n",
    "In every state you can choose between two actions (Lazy or Productive). \n",
    "Taken actions impact the state transition probability to the next state.\n",
    "If you for example have a \"Hangover\" and decide to be \"Productive\", there is a 30% chance for you to \"Visit Lecture\" and a 70% chance to stay in the \"Hangover\" state.\n",
    "\n",
    "Define the lazy state transition probabilitiy $\\mathcal{P}_{xx'}^{u=\\text{Lazy}}$ and the productive state transition probability $\\mathcal{P}_{xx'}^{u=\\text{Productive}}$ of the graph shown in the figure below.\n",
    "\n",
    "![](Hangover_MDP_Graph.png)\n",
    "\n",
    "With $p_k = \\begin{bmatrix}\n",
    "\\text{Pr}_k \\lbrace \\text{Hangover} \\rbrace \\\\\n",
    "\\text{Pr}_k \\lbrace \\text{Sleep} \\rbrace \\\\\n",
    "\\text{Pr}_k \\lbrace \\text{More Sleep} \\rbrace \\\\\n",
    "\\text{Pr}_k \\lbrace \\text{Visit Lecture} \\rbrace \\\\\n",
    "\\text{Pr}_k \\lbrace \\text{Study}\\rbrace \\\\\n",
    "\\text{Pr}_k \\lbrace \\text{Pass the Exam} \\rbrace \\\\\n",
    "\\end{bmatrix}^\\text{T}$"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## 4) Solution\n",
    "\n",
    "\n",
    "\\begin{align}\n",
    "\\mathcal{P}_{xx'}^{u=\\text{Lazy}}&=\\begin{bmatrix}\n",
    "0 & 1 & 0 & 0 & 0   & 0\\\\\n",
    "0 & 0 & 1 & 0 & 0   & 0\\\\\n",
    "0 & 0 & 1 & 0 & 0   & 0\\\\\n",
    "0 & 0 & 0 & 0 & 0.8 & 0.2\\\\ \n",
    "0 & 0 & 1 & 0 & 0   & 0\\\\\n",
    "0 & 0 & 0 & 0 & 0   & 1\\\\\n",
    "\\end{bmatrix}\\\\\n",
    "\\mathcal{P}_{xx'}^{u=\\text{Productive}}&=\\begin{bmatrix}\n",
    "0.7 & 0 & 0   & 0.3 & 0   & 0\\\\\n",
    "0   & 0 & 0.4 & 0.6 & 0   & 0\\\\\n",
    "0   & 0 & 0.5 & 0   & 0.5 & 0\\\\\n",
    "0   & 0 & 0   & 0   & 1   & 0\\\\ \n",
    "0   & 0 & 0   & 0   & 0.1 & 0.9\\\\\n",
    "0   & 0 & 0   & 0   & 0   & 1\\\\\n",
    "\\end{bmatrix}\n",
    "\\end{align}"
   ]
  },
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   "source": [
    "## 5) Markov Decision Process: Trivial Policy Evaluation\n",
    "\n",
    "The rewards for this problem are defined by:\n",
    "$r_\\mathcal{X} = r_\\mathcal{X}^{u=\\text{Productive}} = r_\\mathcal{X}^{u=\\text{Lazy}} = \\begin{bmatrix}\n",
    "-1\\\\\n",
    "-1\\\\\n",
    "-1\\\\\n",
    "-1\\\\\n",
    "-1\\\\\n",
    "0\\\\\n",
    "\\end{bmatrix}$.\n",
    "\n",
    "How can we interprete these rewards?\n",
    "Evaluate both the lazy policy and the productive policy using $\\gamma = 0.9$.\n",
    "\n",
    "Bonus question: Can we evaluate the state-value of $\\lbrace x=\\text{More Sleep}, u=\\text{Lazy}\\rbrace$ for an infinite time horizon without the use of the Bellman equation?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-ec5a4ac90a58ac12",
     "locked": false,
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   },
   "source": [
    "## 5) Solution\n",
    "\n",
    "The given rewards can be interpreted as a penalty for wasting time. The earlier you pass, the better :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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     "grade": false,
     "grade_id": "cell-7da3b2398127ac8e",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-10. ]\n",
      " [-10. ]\n",
      " [-10. ]\n",
      " [ -8.2]\n",
      " [-10. ]\n",
      " [  0. ]]\n",
      "[[-4.15414315]\n",
      " [-3.05228771]\n",
      " [-2.71728272]\n",
      " [-1.98901099]\n",
      " [-1.0989011 ]\n",
      " [ 0.        ]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "### BEGIN SOLUTION\n",
    "\n",
    "P_xx_lazy = np.array([[0, 1, 0, 0, 0,   0],\n",
    "                      [0, 0, 1, 0, 0,   0],\n",
    "                      [0, 0, 1, 0, 0,   0],\n",
    "                      [0, 0, 0, 0, 0.8, 0.2],\n",
    "                      [0, 0, 1, 0, 0,   0],\n",
    "                      [0, 0, 0, 0, 0,   1]])\n",
    "\n",
    "P_xx_productive = np.array([[0.7,  0,   0,   0.3, 0,   0],\n",
    "                            [0,    0,   0.4, 0.6, 0,   0],\n",
    "                            [0,    0,   0.5, 0,   0.5, 0],\n",
    "                            [0,    0,   0,   0,   1,   0],\n",
    "                            [0,    0,   0,   0,   0.1, 0.9],\n",
    "                            [0,    0,   0,   0,   0,   1]])\n",
    "gamma = 0.9\n",
    "r_X = np.array([-1, -1, -1, -1, -1, 0]).reshape(-1, 1)\n",
    "for P_xx in [P_xx_lazy, P_xx_productive]:\n",
    "    v_X = np.matmul(np.linalg.inv(np.eye(6)-gamma*P_xx) , r_X)\n",
    "    print(v_X)\n",
    "    \n",
    "### END SOLUTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "grade": false,
     "grade_id": "cell-f336cf334f83fe6d",
     "locked": false,
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     "task": false
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-10.000000000000002\n"
     ]
    }
   ],
   "source": [
    "# Bonus question: Can we evaluate the state-value of {𝑥=More Sleep,𝑢=Lazy} for an infinite time horizon without the use of the Bellman equation?\n",
    "\n",
    "\n",
    "### BEGIN SOLUTION\n",
    "# We can evaluate the \"More Sleep\" state for an infinite time horizon because the reward will stay the same forever.\n",
    "# Thus, we can make use of the geometric series convergence. Due to numeric inaccuracy results may deviate slightly.\n",
    "import numpy as np\n",
    "\n",
    "v_3 = -1*1/(1-0.9)\n",
    "print(v_3)\n",
    "\n",
    "### END SOLUTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-0058e2bd53d74195",
     "locked": true,
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     "solution": false,
     "task": false
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   },
   "source": [
    "## 6) Action-Value Function Evalution\n",
    "\n",
    "Now, the policy is defined by:\n",
    "\\begin{align}\n",
    "\\pi(u_k=\\text{Productive} | x_k)&=\\alpha,\\\\\n",
    "\\pi(u_k=\\text{Lazy} | x_k)&=1-\\alpha, \\forall x_k \\in \\mathcal{X}\n",
    "\\end{align}\n",
    "\n",
    "Calculate action-values for the problem as described using the 'fifty-fifty' policy ($\\alpha = 0.5$) according to the Bellman Expectation Equation: $q_\\pi(x_k, u_k) = \\mathcal{R}^u_x + \\gamma \\sum_{x_{k+1} \\in \\mathcal{X}} p^u_{xx'} v_\\pi(x_{k+1})$ $\\forall x_k, u_k \\in \\mathcal{X}, \\mathcal{U}$."
   ]
  },
  {
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    "nbgrader": {
     "grade": false,
     "grade_id": "cell-63b67694ef859466",
     "locked": true,
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     "solution": false,
     "task": false
    }
   },
   "source": [
    "## 6) Solution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-c39018db625f6a27",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-6.07176629 -5.82932926]\n",
      " [-6.07830245 -5.1922893 ]\n",
      " [-6.07830245 -5.20681411]\n",
      " [-3.66826061 -4.33532576]\n",
      " [-6.07830245 -1.33353258]\n",
      " [ 0.          0.        ]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "gamma = 0.9\n",
    "alpha = 0.5\n",
    "no_states = 6\n",
    "no_actions = 2\n",
    "r_X = np.array([-1, -1, -1, -1, -1, 0]).reshape(-1, 1)\n",
    "q_XU = np.zeros([no_states, no_actions])\n",
    "### BEGIN SOLUTION\n",
    "\n",
    "P_xx_lazy = np.array([[0, 1, 0, 0, 0,   0],\n",
    "                      [0, 0, 1, 0, 0,   0],\n",
    "                      [0, 0, 1, 0, 0,   0],\n",
    "                      [0, 0, 0, 0, 0.8, 0.2],\n",
    "                      [0, 0, 1, 0, 0,   0],\n",
    "                      [0, 0, 0, 0, 0,   1]])\n",
    "\n",
    "P_xx_productive = np.array([[0.7,  0,   0,   0.3, 0,   0],\n",
    "                            [0,    0,   0.4, 0.6, 0,   0],\n",
    "                            [0,    0,   0.5, 0,   0.5, 0],\n",
    "                            [0,    0,   0,   0,   1,   0],\n",
    "                            [0,    0,   0,   0,   0.1, 0.9],\n",
    "                            [0,    0,   0,   0,   0,   1]])\n",
    "\n",
    "P_xx_mean = alpha*P_xx_productive + (1-alpha)*P_xx_lazy\n",
    "v_X = np.linalg.inv(np.eye(6)-gamma*P_xx_mean) @ r_X\n",
    "transition_tabs = np.dstack([P_xx_lazy, P_xx_productive])\n",
    "q_XU = r_X + gamma * np.squeeze(np.transpose(transition_tabs, [0, 2, 1]) @ v_X)\n",
    "\n",
    "print(q_XU)\n",
    "\n",
    "### END SOLUTION"
   ]
  },
  {
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    "nbgrader": {
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     "grade_id": "cell-234818bf849f0638",
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   },
   "source": [
    "## 7) Markov Decision Problem: Stochastic Policy Evalution\n",
    "\n",
    "Plot the state-value of the states \"Lecture\" and \"Study\" for different $\\alpha$. What do we see? Why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-f8b7c0c069fbda39",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "## 7) Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-58dae08eed6fcc5a",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
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   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-v0_8-talk')\n",
    "\n",
    "n = 6 # dimension of state space\n",
    "no_of_samples = 1000\n",
    "\n",
    "alphas = np.linspace(0, 1, no_of_samples)\n",
    "v_n_alpha = np.zeros([n, no_of_samples])\n",
    "\n",
    "### BEGIN SOLUTION\n",
    "gamma = 0.9\n",
    "P_xx_lazy = np.array([[0, 1, 0, 0, 0,   0],\n",
    "                      [0, 0, 1, 0, 0,   0],\n",
    "                      [0, 0, 1, 0, 0,   0],\n",
    "                      [0, 0, 0, 0, 0.8, 0.2],\n",
    "                      [0, 0, 1, 0, 0,   0],\n",
    "                      [0, 0, 0, 0, 0,   1]])\n",
    "P_xx_productive = np.array([[0.7,  0,   0,   0.3, 0,   0],\n",
    "                            [0,    0,   0.4, 0.6, 0,   0],\n",
    "                            [0,    0,   0.5, 0,   0.5, 0],\n",
    "                            [0,    0,   0,   0,   1,   0],\n",
    "                            [0,    0,   0,   0,   0.1, 0.9],\n",
    "                            [0,    0,   0,   0,   0,   1]])\n",
    "r_X = np.array([-1, -1, -1, -1, -1, 0]).reshape(-1, 1)\n",
    "\n",
    "alphas = alphas.reshape(-1, 1, 1)\n",
    "P_xx_mean = alphas * P_xx_productive + (1 - alphas)*P_xx_lazy\n",
    "v_n_alpha = np.squeeze(np.linalg.inv(np.eye(6).reshape(1, 6, 6) - gamma * P_xx_mean) @ r_X).T\n",
    "\n",
    "### END SOLUTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-ce3713de655bb104",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-10.0, 0.0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kCS666KJmfw96vR7nnXceNm/ejIULF+Kuu+5CQUEBPvzwQ4wfP17tCxwxYkSzj6326KOPoqSkBHfccYf68yeffIIrrrgCOp1O7S8kIiIiohZU7ApMFTsPfye4+ziZ3X9gfQoOb0pr8vNJpc+pl9bstlm3xsDR1R5ax9MRrLCwEG3Npkq+O2o38h/Vw8NDvbl3d3dv8LjMzEz4+/ub32g0AmV5sFpOXkADYaopDAaDqq7J9y7NUz766CMV/CQIPv300xYfc/LkSURERDT5NeLj49GzZ0+L923ZskXt4ZMw1rt3b0yYMAEjR47ElClT0K9fv3pdOmU/n4Su6rD1+uuv44EHHsCyZcswc+ZMs//mAwcORGRkpHpcc4+VCt/111+vqp/79u1T//8I+X9IqnxFRUVISUlRoZma8ftERERE3XcpZqppKaYEvDxZlplWgvLSSnWMVN0GnxNm9rg9fyVi0w/H6j2fvaMG3kEu6uIVKBdnVclz93WCra1Nq2WDlmCFrzORsPdKJKzWwycAF5+zfrgs25QOmbKs8YILLsCaNWtw9913Nxj2qpdZrly5ssmv0diyzLFjx+Lvv//Gq6++iqVLl6owJxcxceJEFbwkiDXkyy+/RN++fTF8+HBkZ2eb3Xfuuefis88+U0tXJZg159hqt99+e03YE/L1bbfdhieeeEKFw1mzZjX550BERETUXZTkl2PXigTkpBQjJ6VELblsjIS/unzD3BAQ4Q7vYFO4U5dgF7h4OqitN9aMgY+sypw5czB06FCsXr1aLeF88803Gz1e9r5Nnz691V4/JiamZrZeQkIC1q1bpyqNGzZsUCFUAqFWa16er3b48GEV0vz8/Bp8fgl3YWFhzTq2Wt0qo+jfv7+6loooERERUXckTVIKs8qQk1qM8pJK9J8QbH6ADbBvdXKTnkvrqIGl/BbaxwuXPGq+3aazYOAjq/Ltt99i79696msZg3CmT0xkGWhWVlaTn18CllQSm0Kao8h4BtljJxW+TZs2Yfv27WqpZ0NLBCQwvvbaa42+fnOPJSIiIiKTsqKKmkqd6bpYLc2s1Btrllj2Gx9k9h7S2V2r9s7VruxJsKup1gW7wivIGd5BrnDx1Fp9xa65GPg6E9kjJ8smrfn8WkBm3UnAmj9/vpqF9/HHH6s9fJYqW9WSkpJabQ9fQ+SXfvTo0SrwyV65hsi+Pwmf06ZNa7AxzNkcW02qglJlrO3QoUPqurGlpkRERESd2YF1yTixJwvZKSUoKzTvflmXXmdAUY5O7aGr/V5u8LQw2NgCPiGu6uLqZf1LMVsLA19nIsGgBXvkrNm2bdtUZ0zpUPnVV18hOTkZP/74o+rS+fPPP7fLHj55HmnEYmdn/mshSy8ljNZeQmmJhNWHH35YVe0eeuihevdnZGQgICCg2cdWe++998z28cnm3vfff1+Nc5BmM0RERESdUYWuUlXpspOKENTLUwWy2uS+pMNnblwoIU6qddXVvtpGzG7eB/5dCQMfdTipUs2ePRvR0dEq3MmMORmRcOONN6pAI5U1CYJtvYdPqok5OTmYN2+eWm7p7OysKohff/01jh49qkKa3N6Qe++9V4VGCXKyB1Gqd9JtKTExEatWrVLnKo1omntsNV9fX1VplI6dQhrKyPGyx1DOlYiIiMjaSYdMCXZZSUXITi5GdlIx8jNLgVNzA8ZcGFkv8PmGu5n92d5BA58QF3iHuMJXVexMyzIdXezb81vpNBj4qENJYJGxBF5eXqozZu12tE899ZTqVvnII4+o0NfWpNr2yy+/YOPGjaq6mJ+fr6ppMvpAZuCdadadLEP9448/1BzBL774As8884y6PTg4GKNGjcK11157VsdW+/e//62ax8hMQqkASkCWauiVV17Z6j8LIiIiotYgA8j3/JWkgl12cpGaddcYCYF1Bff2xKi5ESoI+oa6ws3bUQ0yp6bhHL7ONIePuqXqOXxS8ZOZgNR8/H0iIiJqO1XGKlWlK8zWocdA8+1H5WWV+Oj+9Wd8DluNjWqi0jPGF6PndZ/eBIWcw0dERERERNYU7gqyypCZWIishCJkJpiWZ0qzFI2dLW5+cxI0mtMN6Ryc7ODu66jCYDWtk52q1PmGucIvzE1dy9ByeTy1PgY+IiIiIiKyqChXh/QTBaZgdyrkVegMFo81VBpVgxUJcbXJXLzKCmNNwHPzcew2HTKtAQMfEREREVE3J7u8jJVV0NibV9n2r0nG7pWJTXoOCXK1Z91VG35e9+2QaQ0Y+IisnDSLOVPDGCIiIqLm0JXokXGyEJknC5ERX6i+jpkSilFzzOcb+/Uwr9ZVc/V2gH8Pd/iFu8G/h5u6dnLVttPZU3Mw8BERERERdWEGvRFZyUWnw118odqHV5fcX5eEOZlvZwp27ioA+ku4c2O46ywY+IiIiIiIuiCjsQpLXvkbWYlFMBrO3JhfgqAs7ay9v87DzxnXvmh5HjJ1Dgx8RERERESdeWlmfKFqrDL8vB6w02pq7rO1tUFlhaHBsGfvqFFVu4Ce7giIMF2zmUrXw8BHRERERNSJ5t2lHS9QAS/9eAHy0ktr7pcZeIGRHmaPkRCXk1KiBpX7hLjUCnce8Ax0VqGQujYGPiIiIiIiK6SvMNRU76ov5SWVDR4vx9YNfIOmhaHv2CD4hrvBvlb1j7oPBj4iIiIiIiuUdCgXS9/ff8bjZGmmVO6cPeo3UvEJcW2js6POgoGPiIiIiKidGQ1GZCUVI+1YvlqaKfPvzr1hgNkxdat11Tz8nBAY5aHul4t3sAuXZlKDGPiIiIiIiNqYNE+RJZepx/JVyEs7UYjKcoNZlU66atYObs7uWngFucDRxa4m3MlFbidqKgY+IiIiIqI2kBqXj4QD2UiNK0BmQmGjoxH0OgNyU0vgG2q+BPOKp0aphitEZ4uBj4iIiIioFTpo1g1mR3dk4OD6lIYfZAMV8IJ6eSIo0gNu3g71D2HYoxZi4CMiIiIiaqbCnDKkHs1HSlw+0uLyMWBiCIbOCDc7JriXh1ngs7WzQUAPdwT19kRwL0+1D8/BiW/HqW3x/zAiIiIiojMoztMhJTYPyUfzkXo0D4XZOrP7ZW9evcDX2xNh/b1VuAvu7QH/nu6ws+doBGpfDHxE3dzatWsxdepUfPLJJ7juuus6+nSIiIisQlVVFeJ2ZKiQl3I0HwVZZY0eL41Y6i7rdPVyxLx7hrTD2RI1zLaR+4i6XfCxsbFRl7vuusviMZmZmdBqteqYKVOmwFqdOHECt9xyC/r27QtnZ2d4eXmhX79+uPbaa7FmzZqOPj0iIiKrJ//Wb/v1BA5tSmsw7Ll6OSB6dACmXNUHFz8yXO3JI7I2rPAR1eHo6Iivv/4ar776KhwczDdPf/HFF+oTPzs76/3V2blzJyZPngx7e3tcc801GDBgAMrKyhAXF4cVK1bAzc1NVfSIiIi6M12xHsmqepenqngzbx5Yb0h5SB8vFGan1fxZBpuHRHshtI8XgqM91Tw8CYZE1sx637USdZD58+fjm2++wS+//ILLLrvM7D5Z9jh79mysWrWqVV/TYDCgvLxcVeNa6rnnnkNpaSn27NmDwYMH17s/PT29xa9BRETU2VTqDUg7VoDkI7lIOpyHrKQioNaUBAl/dQNfzxhf6MsNKuSFRHvCM8CZAY86HS7pJKsgFajQ0FCEh4er4FPbTTfdBI1Gg8WLF7fLuQwbNgyDBg1S4a627du34+DBg7j++ustPi47Oxt33nknwsLC1LJPuZY/5+TkmB336aefqn8s/vrrL/zzn/9EVFSUqip+9913NcfIz+CFF15Q1Tm5z9PTE3PnzsXu3bvPeP5SyfPx8bEY9kRgYGCTfg7NOYemHlv7e3/22WfRo0cPVUWVn3d7/fclIqLuQfbTZSUWYdfyBPzyxm589MAG/PrmHuxanqhurx32hFT56ooc4oeZNw3EwEkh8Ap0YdijTokVPrIKTk5OqjIl4e7dd9/F/fffr25//PHHsWjRIrzzzjtYsGBBvccZjUbk5uY2+XW8vb1ha3vmzzluuOEGPPDAA0hJSUFISIi67eOPP4a/vz/mzJlT7/iCggKMGzcOx44dU4+V0Chh57333sPq1atVWJSllLU99NBD0Ov1uPnmm+Hu7o4+ffqo2+W28847D5s3b8bChQvVfkJ5/g8//BDjx4/H+vXrMWLEiAbPXQJkbGwslixZgosuughnoznncDbn++ijj6KkpAR33HGH+rOE6yuuuAI6nY6NY4iIqNXGJnz3wo5Gj3FwtjNV7/p4IrSvd7udG1G7qqJ2VVBQIJ8nqevGZGRk1LvNYDRU5ZblWu1Fzq8lKisrqwYMGFDl5+dXVVRUVPX666+rn9Vzzz3X4GPi4+PVMU29yPENWbNmjTrmlVdeqcrOzq7SarVVzz//vLqvtLS0ysPDo+rBBx9Uf3ZxcamaPHlyzWOfeOIJ9dh33nnH7DnffvttdfuTTz5Zc9snn3yibouOjq4qKSmpdx6vvfaaun/ZsmVmt8v/M2FhYWava8nmzZur7O3t1XP07t276vrrr6969913qw4dOtTo9y3ndTbn0Jxjq7/38PDwqvz8/Jrb5Wu5zcvLS/2sW5ul3yciIur8yoorquJ2ZlRt/P5oldFoNLtP/vzZE5uq3r51Vc3l3TtXV/302q6qnUvjq9LjC6oMBvPHEFlrNmgJVvikhJ+Sgs8//xzLli3D0aNHUVhYiJ49e6q9Wo899phaHmcNCsoLMOnbSbBW6y9fDy9Hr7N+vCzbfOmll9RSwAsuuEB1k7z77rvx9NNPN/gYWZ64cuXKJr9GU5czyn/zefPmqSWITzzxhKqWSdVKqneW/PTTT/Dz81OdMWu79dZbVeVS7pflm7XdfvvtFvfsffnll6q75vDhw9Uy0drOPfdcfPbZZ2oJrFRFLRk7diz+/vtv1XRm6dKlqnpWvTx14sSJ6nuKjIxs9PtvzjmczfnK9+7h4VHzZ/n6tttuUz9r6ZY6a9asRs+PiIi6J6OxCpknC5FwMAeJB3ORmVBYszSz37hgeAe71Bwryy/D+nmrY8L6eiO0nxeCennCXss5eNS9MPAB+O2339R+ovPPPx8PP/ywWnonS/DeeOMNta9ox44dTQ4K1DKyXHLo0KFqGaQs4XzzzTcbPV72i02fPr1NzkX26sn/Exs3blTLOUeNGoX+/ftbPDY+Pl4tW6zbvVP+HB0djV27dtV7jNxuyeHDh1VAkgDZEAlWskewITExMSrYiYSEBKxbtw4fffQRNmzYoMK0BELZZ9iQ5pzD2ZyvjIioq/pnKyMliIiIqpUUlCPxVMBLOpyL8tJKi8clHck1C3xiypV9zObiEXVHDHynqh7yprh2qJN9VaNHj1bX//nPf9SF2t63336LvXv3qq8leJ9pc7R0t8zKymry80sokUpiU8ycOVPt35MKnVQbZT9ea2qoI6eMfZDA9tprrzX42MbCVV3SGEXGM8j+Ovl/fdOmTeoDjQkTJjT4mOacQ2ufLxERkUg7XoB138QiJ7n4zPvw+njB07/+v6sMe0QMfIp0FrTk8ssvV4HvwIEDsAYeDh5q2aS1kvNrCZkRJ8FExiLIDDmpqknzFkvVoGpJSUmIiIho8mtIJU6W6zaFBEM5nxdffFEtR5SmIg2RJZLSKKWystKsyid/lmXCZ1pCWVvv3r1ViJ02bVqTGsw0lYRn+RBDAp8sY26tczib85WqoFQaazt06JC6bs7PioiIuobyUj0cnO3NbnNytbcc9myAgJ7uCB/gg/D+3vDv6Q5bBjuiBjHwNSI5OVldBwQEwBrY2ti2aI+cNdu2bZvqKCldHb/66iv1s//xxx9Vl86ff/653ffwVZN9ZbL0UUKIdNJsyIUXXqjGEsiySXlMNelUKWFI9vI1lYRMWVosFTPp5FlXRkZGo/9Pys9DBqvXXV4qyy4lVIuGlqaezTmczflKtbT2Pj7ZH/n++++rcQ4yNJ6IiLo2Q6URqXH5SNifo/bjVVYYcM0L48xW9nj4O8Hd1xGF2To4uWvRo783wgf6qP14jq7m4ZCoMzDqdNAnJ6MiORn6pGS4nzcTcHBo89dl4GvEM888o66vvfbas36OoKCgemMECPUqO9IgR/a0SbiTuWwyWuDGG29UIUAqUhIE23sPn5C5gLK/80weeeQRfP/992runuzXk32IMpZBRkrIuAW5v6nuvfdeFdokRMleRqmcSdhMTExUA9/le5Ylpg2RqqjM/pOmM7LUUpaOSiX066+/VtVGCWhye2udw9mcr6+vr6o2Vs80lKYycrwE5tYYPk9ERNantLACCQdykLA/G4mHc6HXGczuz00rgU/w6cHnEv4mX9kHTm5a+Ia4cnkmWb0qoxGVmZnQJyWhIjnl1HWSCncS9CrrbEPSyqqzwYPa/Ly6VODLz89XjVaa6p577lFz2SyRDofyBl66LsobWGob8iZf9sp5eXmpjpK1q2hPPfWU6vAoYUlCnzWTSpWco3xI8Ouvv6oAI1UtqfbJHsC6M/gaI8tZ//jjDzWP8Isvvqj54CE4OFg1jjnTBxBSafvll19UsxmpksrvhZyfDDeX+XdNmXPXnHM4m/P997//rRrIyHxFqQBK2JfK7pVXXtnknxMREVk32eOdnVSMhAPZOLk/BxknT3fUtCT1aL5Z4BPh/a2jUzpRNUNxsQpyNlotHKKiUFvBkiVIe/IpNJU+JQWadgh8NjKbAV3EyZMnm7WfKy4uDr169ap3u1QZJOhJ1Una6csb2tYiIx/kzbcsYWtsiWBmZqYa8k3UlUjnUKnqScVvypQp7fa6/H0iImp/B9anYN3XsQ3eL/vugqM90WOgj7p4BjifsVkbUVurMhhQmZ6OisREVCSdqs6lJKNCrpOSYMjPV8e5z5mDkP+8YvbYkq3bkNiED9bt/P1hHxoK76uvAiZMaFI2aIkuVeGTZhwtza/SKETC3owZM1R1pDXDHhEREVFXU5SrQ2F2GUKizfsMhPWr33fAyc0ePQb4oEeML8L6e8PBqUu9FaVOFOps6nRtr6qsxIkLL4Q+MQlVFRVnfA4Jf3VJiBO2zs6wDwuDfVgotKFh6nZtWKjptuBg2Do6mhWD2hp/y+qEvZtuukntCaveS0ZEREREp8mH6zkpxTixJxvxe7PUsk0XTwdc+2Kdpit+zvAKdIatnS16xvigZ4wvO2pSu6nS66FPTTVV6hISUZGYgIqEBOgTEqFPS0OfHdvVssxqNnZ2MBaXNCnswdZWhca67IOD0HvLZmg8Pa2qWs3AV2upmYxgkP16sv9JGk0QEREREWA0GNVcvPg92TixNwtFOTqz+0vyy5GdXAy/MPM965c+PhL2Dk2bf0vUEsXr16N43XpTwEtMgD4lVeZjoSEVKSlwqLMVTBserpZzClt3d2hVlS7MVJ0LPXUttwUGwsbCKkAbW1vYeVlfR30GPkA12ZCOkLJuVmbvyVLO2lxdXVXbfSJqGWkY05SmMURE1PH0FQYkHcpVVbyT+3KgK9E3eKyrl4MKfXUDH8MetUZFWTpfVsTHmy4nT8J1yhS4jB1rdlzprl3I++qrJj9vRUJCvcDn/+AD6to+PNwqg9vZYuADVBt9GZcg3Qxl/15dPXr0YOAjIiKibuXQxlRs/C6uwft9QlwRMcQXkYP94BvmalVL2KjzMRSXqDCnLqfCXflJCXgJqCotNTvWxtGpXuDThvew+LwaHx9VuZOLfY9waHv0UMc6REXWO9Zp8GB0RQx8gJqz1pRZa0RERERdselKQWYpQvuaj6qKGOxrFvgkzwX18kTkED91n7uvUwecLXVm0hilMjcX9nU6Z0vIO37erCY/jxxfl+OA/vC46CJTuOsRrqp0Eu40ruajProjBj4iIiKibqYgqwzHd2fi+K4sZJ4shIuHFte+ON5suLm7jxMCIz1UZ82IwX7oOcgHTq6nm1wQNTarruLECZQfO46KE8dRHn+qapeUBDtfX/Res9rseOlcCemaaaERSjVbV1doIyLUsHLnkSPq3e/Ypw+CX3i+Tb6fzo6Bj4iIiKgbyM8orQl5WYlFZveVFFQgM6EIARHmc8AuengYl2rSGRWuWIHSHTtRcfw4yo8fR2VGRoPHVqalwVhaqkYXVJNumfahIarRilZGGJwKdtqInmqfnXyt8fXl/4tniYGPiIiIqIvKTS2pCXkySqGxpitlRfXb0fMNNqmmKRkZNdU610mTVACrrWjFShT+/nuTn1MqfVKRq63H51/AztvLYvdLahkGPiIiIqIu6PDmVKz+/EiD97v5OKLXMH9EDfOHf083hrtuTubK6ZOTVYVOLhXHT5iuT5yAsaSk5jgbR8d6gc9SAxTY26u9dA6RUdBGRpgqddXVOnf3+ocHmO/ro9bDwEdERETUySswumI9nNzM99eF9TNvwiI8/JwQNdxfBT121iShO3QIqU8+qQJeVXn5GY+XZZt1OQ0dBve5c+EQFQVtVKTpOiyM1TorwcBHRERE1En35MXtzEDczkzodZW45vlxZk1XXL0cERjpjvLSSlXFk4tPiAtDXjciHTHLj8ahPM50kb1ygU/+w+wYWzc3lB86fMbnsvXwgENkJOxDQurd5zJmtLqQdWLgIyIiIupEIxSO7cxUQa9u45W04wUI7u1pdtucu4fAwYlv97pFV8xjx6A7ehTlccdqAp4hJ8fsOI23d73AJwHOxskJVWVl6s92fn7QRkWpcKftJddRcOgVpebZ8cOCzol/AxARERFZsZKCctV05djODBXqGpJyNK9e4GPY6/qS7roLxX+tatKxhtxcVObkwM7Hp+Y2G1tbBL/4Iuz8fOHQqxc0Hh5teLbUEfi3ANFZWrt2LaZOnYpPPvkE1113XZs/joiIuhfpqrnx+zikxOahqsryMbInr/fIAPQa4Q+fYA6Y7koq8/JQHhsL3ZEjKI89Cl3sEQQ+8QScR5jPoNN4mof8umT8gbZ3Lzj07g3H3r1hY1f/7b/7eTNb/fzJejDwEQG49NJL8cMPP2D37t0YMmRIg5viIyMjkZeXh7S0tFZ9/T179uDnn39WAbBnnc5XZwqOr7zyCh566CG0p7M5XyIiah4HZ3skx+YBVfVHKPQaEYDeI/zhF87uml2hO2ZFQgLKjxyB7kisCnblR2ItzrLTHTxYL/BJiJO6rzRIUUsxe/c+dZGQFw374CBVxaPui4GPCMCNN96oAp9U3d58802Lx6xZswYnT57ErbfeCicnJ0yaNAllZWWwb2YHKkuPkwD13HPPYcqUKZ0iQHW28yUislZVxiqkxOXj5L5sjL+4V52mKw4I7uWJ1Lh8OLnZo9dwU8gLjPQwO446r7I9e5Bw7XVN6o4pJBDW5X7++XCZOBHa8HCL1Tsi/l9BBGDGjBkICwvDV199pSpmWq15a2shYbA6HApbW1s4Ojo2+7XO9nHdicFgQHl5OZydnTv6VIiI2my55tHt6Ti6PQPFeaY3+5FDfBHc28vsuFFzImA0ViEk2hO2GlZpOtWw8sxM6A4egu7wIegOHYaNRoPQt8w/VLYPC2s07Nk4O8MxOhoOffvAsW9fOFlYhWTn66suRA3h3xxkFaTiFRoaivDwcPVGv7abbroJGo0GixcvbrPXlxAmyxNzcnLw66+/1ru/sLAQP/74IwYOHIiRI0fWLKmUZTSffvppzXE6nQ7PPvss+vTpo8KKp6cnYmJi8PDDD9ccU/dxcvz111+vvpYlmnKfXFpzf5/8TF944QUMGDBAhU05r7lz56olrHVVVFTg5ZdfVktb5Xvw8PDAiBEj8PbbbzfpfOV++bNUQ+uSaqBUBWuTn4Mc/9dff+Gf//wnoqKi1Dl+9913zT53IiJrVpJfjt0rE7H4X9ux+J/bsWt5Yk3YE7Hb6i/hC+njpebpMexZd7irSEpC4bLlyHz9DSTefAviJkzEsclTkHzHHcj+79soXrUKxRs3ospoNHusNE/R+JnCmn1wMFynTYPvHbcj5M03EbV8Gfrs3IGei79B0LPPwmvBAhX6iJqLFT6yCrJEUpYISrh79913cf/996vbH3/8cSxatAjvvPMOFixYUO9xRqMRubm5TX4db29vFe4skRDzr3/9S1XyLrnkErP7JGxKKK2u7jXkzjvvxMcff4xrrrkGDzzwACorKxEXF4fVq1c3+JiLLrpI7Qn84IMP8MQTT6Bfv37qdgk+rUGv1+O8887D5s2bsXDhQtx1110oKCjAhx9+iPHjx2P9+vUq0FWHvZkzZ6pQKlXPq6++WoWs/fv3Y8mSJeqxbXW+sg9RzvXmm2+Gu7u7Cs3NOXciImtUoavEiT1ZiN2abnE/XjWp4IX2Ma/ukfVLf+EFFPz8C4yFhWc8tqq0FPrERGjrbIUI/2gR7IMCoXF3b8Mzpe6Mga8TMpaVwajTNfl4jZtbvTXdhsJCtUm4qey8vOp9mmXIz1df2zo6wtbJCS0lFaLXX38dL774onrT/9FHH+Gll15SQfCOO+6w+JjExEREREQ0+TXi4+Mb3HMmzyMVq+XLl6tAExQUVHOfhEBZ5ikBqDE//fQTZs2ahc8++6zJ5zRo0CCMHTtWBahzzz23XgWspaQyJwFu2bJlKsxVk5+pVCwlaMn94o033lBfS9CWqlrdcN2W5yuBWqp2tZdxyv8PTT13IiJrU15Wic8e2wR9ueV/b72DXdBndKDqsunmzaX+1qaqogLlx49Dd8i0JNN9zvlwHjq03nGNhT3poOnYvz8cB/SHY79+apZdXY59olv93IlqY+DrhHI+WoTsd95p8vERS35Uf9nUlnzHnSjdubNJj7d1c0OfHdvNb6yqQtzYcepL3zvvhN/dd6GlZNmmBDxZrnfBBReoJil33303nn766QYfExgYiJUrVzb5NeT4xkgFT6pxn3/+OR599FF125EjR7B161ZV9fM9wxp5Wf548OBBHDhwQAUSa/Dll1+ib9++GD58OLKzs83uk8Am4VTCllRZZQ+jl5eXxZ95Q5XR1nL77bfX27PXnHMnIrI2MgPPv6cbUmJNH5AKZ3cteo8KUEHPN9SVHTathHwIrsLdgYPQHdiPsv0HVNfMKr2+5hiZU1c38NV+f2Xn728Kd9UBr39/2AUG8r8xdTgGPrIqc+bMwdChQ1XokiWcDXXMrCbLDadPn95qry/LFWWPmFT0qgOfLNEUN9xwwxkfLxUyWXoo+/ZkhINUDCXAyqWtA1NDDh8+rEKRn59fg8dImJKmNbL8VPbudURTmejo6BadOxFRRy3ZlKHoR7akYcyFUQiKMh9aHT0qEBknixA1xE+FvJC+XrBlh02rIAPIcz78CGUH9qsKniy5bIw0YKnLdfx4hH34garesXEKWSsGPrIq3377Lfbu3au+dpOlqGf4VEy6OWZlZTX5+SU4SCWxIRJ0rrzySrWPUPaNjR49Gl988YVqKFN7SWFDpDIpzUr+/PNPrFu3TjUikT2IEydOVF9b6v7Z1mT5rQTQ1157rcFjGgtUzdXYfzPZ09gQSx052/vciYiaQv5uSjuWj8Ob03BsVxYqTy3ZPLI5tX7gGymjFAJg79Dwvz3Uxt0yMzJQcTIBLmNGm91no9Uit1bjNYtsbKCNiFDVOpexY+rdbefnB1f+O0RWjoGvE/K56UZ4XX1Vs/bw1RX67jvN2sNXj40Nem/ZXLOHrzWsWLFCNTuZP3++mlEnlTVp3lLdFMSSpKSkVtvDV3tZpwQ+qfJJQ5j09HT84x//aHKFThrDyF4/ucg/NI899pjqevnLL7+oAe+WtOVyj969e6tQPG3atDN+D1JlkyWs0hnTwcGhweMaO1/5/oX87Gr/rKWDqeyN7NWrV5ucOxFRWyvK1SF2axoOb0lHYVZZvfvj/s7EhMuizcKdnZZBrz1V5uVBt38/yg4cgG7/AVW9M2Rlq6Hkff7eqUJe7fdHEuYq4uNrbrPvEQ6ngTFwjBkIp5gY1RXT1sWlg74botbBwNcJSYOUljZJaWknKHnDX7eRS0ts27ZNLaeUzouyjyw5OVmNQZDmIT///HO77eETw4YNU8sapdoo5yHfa1OWc0q1saioSC0JrSaPlSWqorFuoq6urmc85mxJiJaxEFIlkyYndWVkZCAgIEB9fdVVV+GRRx5R3UplREJtEl6rg15j51u9NFMqmvKzrN2ApbrxS1ucOxFRW6jUGxC/JxuHN6ci6YjlLpt2WltEDfNHv7FBsLPnh1PtRfbX6WKPomzvHpTt3asu+oTERo91ijHfX+91xRWqGZ7c7jhgADQe5hVaoq6AgY863KFDhzB79mwVFCTcSWVJWvxLpe3999/Hpk2bVBBsjz181eS1pWGMdIeULpSyH+9MJOxJZ8958+apkOfv768qiu+9955qhCL7+Bois/2kgvX8888jLy8PLi4uqnIpS0rPZNWqVap6Vpc0mLnttttw7733qlAswUn2Rkq1TMYeSIdTeaz8DKVBjpBjf/vtNxX4duzYoUYzyP3SiCY2NlaFuDOdr/z3kJEK0vhF5hrK7Rs3blSNb87U9Kau5pw7EVFbWPrefiQesvxhXFAvD/QdG4Rew/2hdeRbqvaW9eabqpFdU2i8vFCZXX8LiPc1C9vgzIisC/92og4lb9xlb5wEoqVLl6o389Weeuop1YVRKk4S+tqTVLokZEiQakp1r3oP2n333aeCiASj4uLimgAolcrg4OAGHysD52UJ67///W/VrVLmz1177bVNCnwSSuVSl4QuCXyyPPaPP/5Qy1RlP+Izzzyj7pfzGTVqlHqdarLHUJbWvvrqq/j666/VnD0JVbK0snrY+pnOV/ZIyvD6e+65B//973/Vc0pwlD2NDQX3hjTn3ImI2kLUcH+zwOfi6YC+YwJV0PMMqL/3mFqHVN1kHELZHlPlznnEcHhfc43ZMY6DBll8rCzBdBwoSzIHwnFgjLq2Cw5mt0zqtmyqZJ0WtZvCwkLVul+GR9cON3VlZmaqChERtRx/n4ioMdnJxTi0MRVaR43qtFm3C+cXT25RQ9H7jQtCaD9vdtlsZfJWVAaSq2WZpwKeLjZWOn3VHOMyfjzCF31k9jh9RiaOTZ0Kh9694TR4MJwGD1LX2shI2HDfN3WxbNASrPARERFRtyNB7tjfmSroZcSbBmdrnewwfHZP2NdqtCJLNa99cRzs7Nl8pbVDXu6nn6mZwGW7d8Nwhj3sZfv2ocpoNAty9gH+ak4wm6pQZ1ZuKG/z12DgIyIiom4jK7EIBzek4OiODOh15t2qK8pkpl4m+o4JMrudYa/lnTM1rq6qU2Y1WV5Z8NNPKD96tOEH2tvDsX8/OA2S6t1g6Y4G1KncMexRZ1JprMTx/OM4kH0AB3IO4GD2QRxJPdLmr8vAR0RERF1aZYVBVfP2r0tB5klTNa8u72AX9J8QjJ4xHJ7d4uWZCQko3bUbpbv+Rtmu3ag4cQI9vv4azsNMXaurOQ0bahb47ENCapZlysWhXz/YNjIiiMjafxcSixKxP3u/CnYS8o7kHoHOYN5oz1DVgjFpTcTAR0RERF1Walw+/nx/H8pLTu8HqyYjFHqN8MeAiSEIiHBnU4+zYKyogO7gQRXsSnfvUteWlmeW7d5VL/C5nTNdzcVzHjZchT977rWmTixfl6/C3b7sfdiftV99XVhh+QOm9sbAR0RERF2WVO4MFeYzQH1CXTFgQjCiRwfCwYlvhc5GeVwc0v/vn6a9deVn3oMkobAu14kT1IWos6kwVCA2N9YU7rL3q4An1bym6uneEwN9B6pLD20PTMTENj1f/i1HREREnV5pYQVit6Vj0LRQaDSn93k5utij98gAxG5PV/PyYiaHsprXDIbiYlTEx8MpJsbsdo2nJ0p37GjwcWp55vBhcB42DE5Dh8Ghl3n3U6LOtDQzuSjZLNwdzj0MvVHfpMcHuQSpYDfAZ4C67u/TH25aN7MunW2NgY+IiIg67Rux9OMFam+eNFsxGqrg5u2ogl1to+ZGYOz8KDi5aTvsXDtTg5WyXbtQun2H6qCpO3wYtk5OiN62FTZ2p9822vn5QRsRocIgNBo49uunlmVWBzzpoEnUGRWUF6j9dtVLM+XrvPK8Jj3Wxd5FhbpBvoMQ4xuDGL8Y+Dp1/L5gBj4iIiLqVPQVBhzdlo79a1OQk1Jsdt+B9cn1Ap+rl2M7n2HnUZmVpYJd6Q657FBLNesylpRAdyQWTgMHmN3u//BDKgxKgxVbZw6hp87HYDTgeMFx7Mncg71Ze7Evax9OFp5s0mM1Nhr09uptCna+MRjkNwgRHhGwtbG+GZAMfERERNQpFOXqcGBdCg5uTLHYhEWWbwb0dEeVsQo2HI7eqIJffkH2e++j4uSZ39zaODtDn5xUL/C5TZvWhmdI1PqKKopU1U7C3Z6sPSrgFevNPzRqSIBzgAp1qnrnF4N+3v3gbN85Puhg4CMiIiKrln6iAHtXJeH47iwV5uoKjHTHwMmhiBrmx5l5dRgKC9VFGxpqfoetbYNhz9bNDc7Dh8N55Ag4jxyplmvWnqFH1FmWfCcVJalgJxU8uT6WdwxVqP93SF1Odk5qaaaq3J0KeP7OnXeZMgMfERERWbVdyxMQvzfb7DaNnS16jwrAoCmh8As/3QChu5Pll6W7dqFk61aUbtsO3aFDcJ02FWFvv212nPOo0TVfa7y94TxihOkycgQcoqNho2Fwps5FV6nDoZxDNQFPqni5uvojQiyJ9IjEYL/B6iLhLsojChrbrvM7wMBHREREVvWpfN0OmoOmhdUEPmcPLWImh6jZeWzCAhjLy1G2ew9Kt29DydZtakwCKs2Xu0oDliqDwSzESVOV4H+/BMeBA6GNjGTXUup0MkoyasKdLM08lHsIlcb6S70tVe+kcifhboj/EHXt4eCBroyBj4iIiDpcZkIh9q1OhtFYhRk3mu8VC4n2RJ/RgQgf4I2oYf6qutfd5S1ejMJly1VHzaqKisYPNhqhT0mBNjzc7GaPCy5o25MkasUPguIL47ErY5fpkrkLKcUpTXpssEswBvsPxhC/ISrgRXtFw862e0Wg7vXdEhERkdWQ/Xjx+7Kx569EpB0rULdJoWnMBZFw93WqOU6qT9Ov74/uqm51TpTt24/SrVstHm/j6GjagzdmNFxGj4Zj//5mIxWIrJ1U6o7kHsHfGX9jd+ZudWnK8kwJcjLnrjrcSfXOvxPvvWst/O0nsmI9e/ZUl7Vr13b0qRARtZrKCgOObE1XjVjyM0rN7quqAg5tTMWYC7vvoG5DQYFanlmyebO6+NxwPbyuuMLsGJcxo1GwZIn6WhqqOA0ZAufRo9XtjoMGwVbL5a7UeZRVlqnumX9n/q0qeLL/Tm47E29H75pwJxcJew4ah3Y5586EgY+olhMnTuCll17C+vXrkZiYCAcHBwQGBmLUqFG47rrrMHXqVHXcnj178PPPP6vbJJAREdGZlRZW4MC6ZDUoXVesr3e/T4grBk0LRfTIAHQnsiSzbO9eFJ8KeLr9B9QyzGpyW93A5zxmDHxuuUUFPKehQ9U8PKLONNxcgp1U7iTkSbOVpuy/C3cLx7CAYRjmP0xdy5+5//TMGPiITtm5cycmT54Me3t7XHPNNRgwYADKysoQFxeHFStWwM3NzSzwPffcc5gyZQoDHxHRGehK9Njy83HEbkmHofJ0kKnWY6APhkwPQ0gfr27x5k32I1XEx6Nk4yYV5kq3b4ex1LzSWZtU+6oqK82WZdr7+8P/gfvb6YyJWiarNAs7M3ZiZ/pOtf/uWP6xMz7GBjbo492nJtzJtZ+zH7qEihIgKxbIOgLE727zl2PgIzpFAlxpaakKc4MHD653f3p6eoecFxFRZ2fvoEHCvmyzsGdrZ4M+owIxeHoYfIJd0d0kXnc9KjMzG7zf1tVV7cFzHT8eLuPGARyTQJ1IZmmmCnc7Mnao65OFlmc+1qa11arZd9XhTpZoumk7+ciVynIgO84U7DIPAZmHTZc8+XmcmgdYfua5gC3FwEdWQSppvXv3hq2traqoyVLKajfddBM++eQTfPXVV1iwYEGbnYO8ro+Pj8WwJ2Rpp3j22WdVOBTVFT9x7bXX4tNPP625Pz4+vl71r6E9eUlJSXjwwQexfPly9cmvVBrfeOMNs2N++uknXHTRRfjggw9w88031zs/qUiWl5er76M7fEJORNbJaDCqfXi1O2nK1zJaYctPx+HgbIeBk0MQMyUULh5dd6+N/F1efjQOJRvWw/3882EfFFRzn/wd7TJ+PAp++un0AzQaOA0erMKdy/hxcIqJYaMV6tIBz9XeVYW64QHDVcAb4Dug8+6/M1QCuSeArFOBToW7I0DOMem61NFnx8BH1sHJyUmFJAl37777Lu6/37RM5fHHH8eiRYvwzjvvWAx7RqMRublNG6opvL29Vai0JCoqCrGxsViyZIkKVg2R+9LS0lTweuKJJ9CvX7+ax5+N/Px8TJo0SYW+2267Df3798e6detUmJQgXG3u3LkqdH788cf1At/WrVtx6NAhPP/88wx7RNQhKvUGHNmSjt0rEzFoaigGTwszu3/AxGBV6es7Nkhdd0WG4mJTo5UNG1C8YSMqT60MsXVxqd90Zdw4lO3ebQp4E8bDedQoaFy7X6WTuk/AkwYrEu6qL709e3e+4eZGI1CQWCvUyfURIDsWMJxhPEpdNhrApxfgGgngW7QlBr5OSF9hUB3OmsrByQ62GvOQU16qV7OOmkLWUDu62tf75FL2ZAg7rQb22pb/wkoDlNdffx0vvviiCjQfffSRaqAiQfCOO+6w+BhprBIREdHk17BUdav25JNPYuXKlbj44otVtXHChAkYOXKk2qdXHerEoEGDMHbsWBX4zj33XHV/S7z88ss4efKkCnLXX3+9uk2+3/vuuw9vvvlmzXF2dnbqfvn5SLiTYFhNQrFGo1E/QyKi9lRRVokD61NUx01pyiL2rExUVTxNrX97HJztVVWvy1XxYmNRvH4DStavR+mePfWGngsJf3UDn/uc8+Exd047ni1RywLejvQd6iJ78RIKE5oU8EYGjsTIgJHqOsIjonN9KF1eBGQcAjIOABkHT10fAiqKmvlENoBXT8C/P+Df7/RFwp6dA1BYCFzPwEd17F6egB1/nPmTlGqXPTESfuHma6D/fG8/UuPym/R4rZMdbn59kvmNVcDHD21UX448vydGzZVPJ1pGAosEPKlkXXDBBVizZg3uvvtuPP300w0+RipeEtKaqnpZpiUS4v7++2+8+uqrWLp0qVpGKhcxceJEtVwzMrLl32dd0u0zICBANYqp7dFHHzULfEKCsPyMJODJeYqSkhJ8++23mDVrFoKDg1v9/IiILJFwt291kuq4KaGvtuK8cvVvTFhfb3RFxpISpL/wAkqkitfIPjzh0LsXHGt9aFitU73xpW4nT5eH7enbsS1tm7ru0gHPaATy4k+Fuupgd+DUPrtm8ggD/PqeCnUS8PoCvn0ArTM6EgMfWZU5c+Zg6NChWL16tVrCWTfw1OXo6Ijp06e32uvHxMSoYCcSEhLU0kqpNG7YsEGFUAmE2laebSSjIKSSKIG3tqCgIHh6eprdJtVM+X6/+OILFfyko+h3332HoqIitRyWiKitFWaXqQreoc1pMOjNO27a2togenQAhs7oAe8gF3RVNs7ODYY9W2dnOI8bC9eJk+A6cQLs+UEcdQKl+lI15FwC3rb0bWroeZcMeLqCWlW76srdIUBf0rzncfEHAvoDftUVO/m6D+DoDmvEwEdWRSpVe/fuVV/LGIQz/cVhMBiQlZXV5Of38/OrF6wa0qNHD1V1W7hwoarwbdq0Cdu3b1dLPRvT2DlXWljq01y33HILLr30Uvz6669q+alU+6Ryef7557f4uYmIGgt62349gbidmaiqsyXATmuL/hOCMWR6ONy8HdHZVRkMKNuzB8Vr16JozRqEvf8/aENDzJuuTJyAgh9Ng88doqPhOmkiXCZMhPOwobDh0HOycnqDHvuy95kCXto27Mvah8qqxt+j+Dj6mAJe4EiMCByBCHcrDnjSOSo/AUjbB6TvMwW79AOm/XfNodGaKnaBMUDAgFOXgYCLLzoTBr5OaOjMHoiZGtqsPXx1zb49pll7+CzciBv+M6FmD19rkFl3ErDmz5+vKleyp02at9TeP1eXNDpprT18DZG/zEaPHq0CX0pKSs1tjTWGEdJMpvZr6XQ61eylV69eZsfLMlHprCnhtXYYlWOloUtdUmn09/dXQW/gwIHqvGT5p+zxIyJqKzJS4eiOjJpO4kI6bsq/R9Kkxcm1c4ccQ1ERSjZuVAGvZN16GAoKau4rXrcW3lddZXa85yWXwGnIELhOnAj7RrYLEFkDY5URsbmxKtxtTd+qhp6XVZ5uDGeJu9YdowJHYXTQaHVttRU86ZCZE3c63KXtNV1LNa853IKBwIGnQ51cfKIAjXkfi86I7xA7IftWaJIim+dbQn7hW/Mf923btqnul+PHj1fjF5KTk/Hjjz+qLp2yx6099vDJ80hnzLrBSTplShgV1Y1SXE91UrPUITQ6Olpd//XXXxg2bFjN7dKQRrqKWgpwsjzz888/r2naIv79739bPE8Jw9Kc5T//+U/NeIgbb7zxDN85EVHLeAW6IGqIH47vzoKLhxZDzg1XVT2tY+d9K1GRkKACXvHadSjdudNiwxUh99cNfM5Dh6oLkbU2FEosSjQFvLStqtlKfnnjvRscNY5qBp4EPLn09eprfV009WWmJZjpe82rd5W6pj+HneOpqt2pUKcuAwDnrrnnWNhUyf8R1G4KCwvh4eGBgoICuLs3vM43MzNTVXG6A+k4KUsmZQmlzKer/rncfvvteP/997Fx40YVBNuaVMtycnIwb948tZfP2dlZVRC//vprHD16VFUfP/vsM7PuoEOGDFEdNV1cXNSfpRIolTqZiSedN++99151u3wPMjpB/rvLfbXn8OXl5annkeqhjGWovn/Lli0qbNY9Xhw7dkwFy+qZfXXvp+77+0TUEvJ3SkpsHnb+eRLjLu4F/x7m/05lJxchM6FIDUzX2FsecdMZ6DMykXjddaiIj2/4IFtbUwVvyhS4TZsKhzqrM4isTUF5gQp4m1M3q0taSVqjx2tsNIjxjakJeIP9BkMrSxitRVk+kL7/VNXuVLjLim3eXDvXQCBokCnUqYAXA3hHynBQdLZs0BIMfO2Mgc+cBCcJczJoXZYmSrfKaqmpqWr5ozRxkfvamlTxfvnlFxXOJHzJckr5byVjGGQfn1TVas/wk/AnVTgJX3q9vmbwupCAeM8992D9+vWqycuMGTNUhU++V0uD1+Xn8MADD9RUEqsHr59zzjkWjxdynzS3kcqgnB81rLv8PhGdLXkrkHQoV3WATj9hWgYVMdgXs28fhM6uSq+HjX2d0UJGI45NnYbKjAyz221dXdXePLcpU+AyaRLsvLza+WyJmk5v1GN/1n4V7rakbsGBnANq6WZjor2iVbgbEzRGzcJzsbeS5kpleUDqHiB1t+ki4a65XTK9IkzhLnAQEDTYdO12+n2ltWLg64IY+Ki1zJ49W1UBJRjL4HpqGH+fiCyTtwAJ+3Ow4494Vbmr64pnRnfKbptqAPr69Sj66y81Iy/i55/Nmq6I9P/7P+R9/Q3sw8PhNnUKXKdOhfOwYWy4QlYtqTCppoIn4xKK9cWNHh/qGloT8KTZio+TDzqcrtC0z6463MlFxiI0Z2C5LMmsCXdyHQM4eqAzKmyHwGc99UwiajKpKi5fvlwtJ2XYI6Lmki6bJ/ZmqaWb2Un13zAGRrpjxPkR8Ars2NlRzaHPzETx6jUoWrUKJVu3Anp9zX3Fq1fBu86sU+/rr4fX1VdDG2GljSiIABRVFKlgtznFFPKSi5MbPd5N66bC3djgsRgbNBahbk1v8tcmKkpMyzJrh7vsONNA56awczLtr6tduZMRCPadvxtwe2LgI+pEpLnN4cOH8dZbb6mlog8++GBHnxIRdbKgd2xXpgp6uan1504F9/bEiPN7IrSPV6cIQeXx8ShetQpFf61CmYz0aWDRkjRmqRv4tGFh7XSWRE1nMBpwMOcgNqVuUss0ZVyCoZE9a9X78MaFjMO44HEY4DMAdrYd9PZerzPNtqsd7rKOyF88TXu8LC+VQBc8FAgeYgp4Pr2sar9dZ8WfIFEn8t5776k9ezLKQbqZNnfEBBF1b3F/Z2DlokP1bg/t64URs3siJNqr01TzEq+/ARXHjzd4jK2LC1wnT4Lb9OlqPx6Rtcopy1HVuw3JG7A5bbNqvtKYENcQjA8erwLeyKCRanxCu5Ou49mxQPJOIEUuu4DMQ4CxsumdMiXQqXB36uLbG7C2rqBdBAMfUSciTWGqG8MQETVX1DB/bPvlBAqzTS3Mwwd4Y8TsCARFeVj1PsO61UY7Pz8YS0vrHavx84XbtHPgNv0cOI8eDVvuxyMrruJtSNmAjckb1ddVjSxxlMYqMgdPAp4EvTD3DqhOF2eeDnfJO4CU3UBF/X2/Ftnamzpk1oS7YaY9eKzctRv+pImIiLpi183DuQiK8oS9w+lPzDUaWxXwju/KxMg5EQjo2QGVgSaev+7AARQtX47CFSsR/vEiaENP70WSAOg+YwZyP/sM2h494HbudLiecw6cBg+GTa1uykTWIk+Xp5ZpbkzZiE0pmxqdiWcDG7VMU/bhSciL8YuBvYSm9px1J2MQVLCTgPc3UJDY9IYqAf3NK3ey587Ooa3PmhrBwEdERNSFJB/Jxfbf4pF2vABj50dh2MweZvf3HRuIfuOCYG1kVIJu3z4ULluOohUroE9NrbmvaPkK+Nx4g9nxXgsXwvOSi6Ht1atT7Dek7kXGIxzKOVRTxdufvb/RKp6XgxcmhExQFwl5no6e7XSiRiD3eK3q3U7TPrymLs30jgJCRwAhw02VO6nk2bOZnLVh4CMiIuoCUuPysf23E0g5erpysHtFIgZODoHW8fQ/99YUjiTkle3ejcLlEvJWojI93eJxcn/dwFd3zAJRR5O9d9V78aSal6vLbbSKN9B3ICaGTMTE0Ino79MftjbtUJ0uLzKFuqTtQNI2IOVvQNdwtdGMk5cp2IWMAEJHAiHDAGfvtj5jagUMfERERJ2YDEqXoJd0OK/efTJWoayowizwWQPZf5f52utqyWZlVlaDxznGxMB95gy4zZjRrudH1NSlxycKTmBt0lqsS16HvVl7Gx187uHgofbgSRVvfMh4eDu2cViSrrX5CafDXeI2IPNg07pmqn13MaeqdxLwRgDekfKJUdueM7UJ6/oXgIiIiJokJ6UYW385gZP7suvd59/THaPnRSCsn7dVVfSq2Tg5oXj1aothT/bhuc2cqUIeq3hkbfQGPf7O/BvrktapoHemuXgyJkECnlTxBvoMhKYtu1BWlpv23km4q74UZzTtsZ49zMOddNDkrLsug4GPiIioEynO02HrzycQuz293uxiv3A3jJobgR4Dfawi6JUfO4bCP/+E58UXwz4kxLzpyuxZyPlokfqz07BhcD9vJtzOPRf2Qda3v5C6t3xdvtqLJwFPlmwW64sbHXxeu4rn6+TbdidWnAUkn6reSRVPRiMYys/8OHtn09LMsFFA6CjT165+bXee1OEY+IiIiDqRygojju7IMAt73sEuGD03EhFDfDs86FUkJqLwz6Uq6JUfPVpT0fO9+Waz4zzmz4edfwDcZs6AfUBAB50tkeWlmvEF8VibvFZV8vZk7Wl0qWakRyQmh07G5LDJGOw3uG0Gn8vyzOw4IHGzaWmmhDxpttIU7qGmcBc+xnQdMBDQtGPXT+pwDHxERESdiGeAs+qyeWhjKtx9HTFqbiR6jwyArW3HBT19RgYKl0rIW6o6bdYlt9cNfA5RUepCZA30Rj12Zeyq2Y+XVJTU4LF2NnYYHjBcBTwJeuHu4a1/QkYDkL4fSJCAtxlI2AKU1l++bXEsQtAgIGz0qcsowOP0SBPqnhj4iIiIrFCl3oAD61IQ1Muz3ry8kedHwDfUFf0nBENj1zFz5wz5+WqEQuHvv6P0779NFQgLtJGRahB6lcEAG00b7l8iaqbiimI1F2914mp1XaRveJC4u9Zd7cObEjoF40LGqT+3+v47WZKpwt1m0xLN8sIzP07GN1QHO7mWzplal9Y9N+r0GPiIiIisiNFgxJGt6djxezyK88oR0scTF9w31GyppquXA2KmhHbokreTly9ARUKCxfvtQ0PhPns23M+fDYfo6A5fZkpULbssG2uS1qiQty1tm6rsNSTCI0IFvEmhkzDEf0jrLtWU8QgS6hK3mAKejEpoyv47mXsXPhYIP1XB8+kN2HbMhz7UeTDwERERWcu+oT3Z2PrLceSll9bcnhKbj+TDeQjr3zHzrmRWXpVeD1sHh5rbJMBJJ82cDz6ouc3O3x/us2apkCfjFBjyyFokFiaqgLcqcZUandDQAHSNjca0VDN0MqaETWndpZqluafDXcImUzfNKsMZHmQDBAwAeowzXSTouQW23jlRt8HAR3TK2rVrMXXqVLPbXFxc0KdPH1xzzTW46667oLHC5UiWzrs2OefKysp2PSciav4svc0/HkPa8QKz221sbdR+Pa8glw7psFnw628o+P03eF1xRf2mK/PmIm/xYrifdx7c55wP5xEjYMNKA1nJhyeHcg+pkCeXY/nHGjzW2c5ZLdWcFjYNE0IntN5SzbI8U7iL3wCc3ABkHDjzY6SCGDz0VLiTy2jTsHOiFmLgI6rjiiuuwOzZs9U/GKmpqfj0009x33334eDBg/ig1qfZ1nreddnyDRiR1SrIKsWWn07g+K7MevdFDfNXs/S8Atsv7OkzM1H4x58o+O1XlB86XHN74a+/1W+60qsXojdugI1W227nR9SQSmOlaroiVbzVSauRXpLe4LEy8Hxq2FRMC5+GMUFjoNW0wv/DugJTYxUJd3KRCl4DlcQadk5A2Eigx3hT9U7m33H/HbUBBj6iOoYNG4arr7665s+33347+vXrh48++gj//Oc/EWCl7cPrnjcRWS99uQHbfjmB/euSYTSYvykM6eOFcRdFwb9HKzeFaICxtBSFK1aoUFeydStgrN9+vjwuTu3X0/boYXY7wx51JF2lTs3Fk5AnnTULys0r5LWFuYXhnPBzVMgb5Duo5QPQZQ9e4lZTuJMqXtoeWf/c+GMcPU7tvxtrCnlBgwE7/g5R2+NH/2QVysrKEBoaivDwcJSXm29avummm9SyxMWLF3fIubm7u2Ps2LGq4nfixAl1W1FREZ588kmMHj0avr6+cHBwQK9evfDYY4+htPT03huh0+nw7LPPqqWhzs7O8PT0RExMDB5++OFmHdNSsqxz/PjxapnqkSNHzO6TyqXst3n66adrbmvO9yhVUHn8qlWr8H//93/o0aMHnJyc1GO3yhtIAOvWrcOECRPU6wcFBanwTNRd2drZIOFgjlnYk2Wb5985CBfcN6Rdwl7Znj1IfeIfiJswEWmPPY6SzZvrhT1tVBT87r8fvVb9VS/sEXWEssoyrExYiUfWPYLJ307GvWvuxa/Hf7UY9vp598OdQ+7EknlL8Mf8P/DgiAcx1H/o2YW9ihLg+Grgr+eAj6YDL/UAvroE2PQmkLrLcthzcAeizwNmPA/csg54JB648ltgwn2myh7DHrUTVvjIKkg4eO6551S4e/fdd3H//fer2x9//HEsWrQI77zzDhYsWFDvcUajEbm5uU1+HW9v72YvcZSgd+yYaf2/BB+RkpKiKn4XX3wxrrzyStjZ2alA8/LLL2P37t1Yvnx5zePvvPNOfPzxx2of4AMPPKCCV1xcHFavXt2sY85EQlh2dv0ZPVqtVoVWOcevv/4aQ4YMUT/Lbdu2qRAnS1VlyaqEsWeeeabmcc35HqtJGDQYDLj33ntRUVGBV199FTNmzMDnn3+OG2+8EbfccguuuuoqfPfddypcRkREsCpJ3ZJGY4ux86Ow9P39cHLXYvTcCLVXz1bTfp/DFv31FwqWLKl/bn6+8Dh/jtqj59CvH5uvUIcr1ZdiffJ6rEhYocYnSOhrrOmKVPFkyWawa/DZv6hBD6T8DZxYa7pIF81GOnoqWldT9a7nBCBiIhA4GNDwrTZ1PJsqeTdL7aawsBAeHh4oKChQb8IbkpmZCX9//3qd0sqKG54R09GcXN1atGFfgsLgwYPV9y6VNAkbEvwkCNauPNV28uRJFRqaKj4+Hj179my0+Ym83h133KGCXlpaGv773/+qcxkzZgy2bNmijpUwI2+C7O3tzZ7jqaeewr/+9S8VpkaNGlUTMuWxf/75Z4Pn1ZRjzrZpy/nnn4/ff/+95s9LlixRIU5C5iuvvIKRI0eqvYp79uxRFdZqzfkepcJ3/fXXY+jQoaqiJyFT/Prrr7jgggtUWJSf3YgRI2qeW6qA8t+i+mfaliz9PhG1l5SjeSjO1aHPmKD6jSU2pqqh6VpHuzZdsqlPT4dDZGS9piwn5sxVX9s4OcHt3OnwmHcBXMaMho0d36RSxyqqKFLLNFeeXIlNqZtQ3sDIAq2tVs3Fmx4+XXXX9JS5dGdD3g5nxZ4KeGuAkxuBiuLGH2PvbBqNIOGu5yQgeAigMf83k6i1skFL8G/0TkTC3ns3XwVrdfuHX8HZ3eOsHy/LNl966SXMnTtXhYQ1a9bg7rvvbjDsicDAQKxcubLJryHHn4lUuWpXuqQiOG/ePLOGLdWBRkg1TpY/SmCdPn16vTAkv8RSRTtw4AAGDhxo8TWbcsyZSPXs0ksvrXe7n5+f2Z8vuugitS9RqqZSqZPX/fHHH83CXnO/x2ryvLUfN3HiRHUtSzurw171c8tjN23adFbfK1FnkJ9Rik0/HsPJfdmwd9QgrL8PnN1P/37IByoDJoa0yWtLmCz7+2/k//QTipYug31YGCJ+/smsWidNVzwXXA6nmBi4zTwPGlc2i6COJcsy1yatVUs2ZW9eQzPyHDWOqrPmuT3OVTPyXOzP8v/dwlTgxLrTVbzihhu9KHaOpgHnEu4k5AUP47JM6hQY+MiqzJkzR1WJZCmjLDt88803Gz3e0dFRBZDWVB2c5I2R7DeLjo5WFbi6ZOnp+++/rwKTLC2tLS8vr+brN954AwsXLlR78iIjI1U1TkKtXKqXlzblmDPp3bt3k38Wr732GlasWIHNmzfj5ptvViHQkqZ+j9Xk3Gvz8jK1k7ZUhZX7cnJymnS+RJ1JeakeO/44if1rkmE0mhbR6HUG7PwjHpOu6NOmr61PSUH+L7+g4KefoU9KOn1OsbEoP3wYjv37mx0f9OyzbXo+RGeSp8tTg9Bluea21G2orLI8RsjJzklV8CTkTQiZAGeprjWXrtA0A+/4GlPAy45t/HgbDRAyHIicAkROBkJl393peZREnQUDH1mVb7/9Fnv37lVfu7m5nXHviFScsrKymvz8Uu060yy9pgQnCUwPPvig2p92zz33IDg4WFWtZN/bddddZxaOpFopS09luabsgfvrr7/UvkSpfsnX8rimHNOa5GecmJiovpaqolTwZNnl2X6P1Rr62Vrj/EKi1mY0GHFoUxq2/XoCumLzyoRvmCsihvq1zeuWl6v9ePk//IDSLaYmSXXZODqqTpt1Ax9RRyisKMSqhFVYfnI5tqZthaGBAeRSuZMB6BLyxgePh6NU2Jq7Dy95h/k+vDMNO/eNPhXwppj24klnTaJOjoGvE5E9crJs0prPryWk4iRNS+bPn6/2jUkTE9nDJyMRGpKUlNRqe/ia44svvlDPs3TpUrMK3LJlyyweLxVCaU4iF1lqJc1NpPnJL7/8UrMMsynHtNZacZnZJw1oZJj8P/7xD7WE9fnnn2/R90jUnSUfycXG7+OQk1JidruzhxZjL4xCn9GBaoh6a9KnpiL3s89Q8PMvMBRYbkfvNHw4POdfCLfzZMmma6u+PlFzG69IJW/ZyWXYlLKpweWablo31XBlRo8ZGBs8tvkz8vITgWOrgGN/AfHrgfLCxo93DTgd8CImAx5ts8yaqCMx8HUi0hClJXvkrJnsB5NlhTI24KuvvkJycrLaVyZdOn/++ed23cPXFFKxkupj7Z5HUiWTPYh1K5Cy903GLFSTx8myVSEdRptyTGsvWU1ISFA/t2nTpql9fHLeUtWs3fylqd8jUXeWn1mKzT8eQ/xe8w65GjtbDJkehmHn9WizhiyGwkLkfvZ5vdvtgoLgceEF8LzgAmhb4QMuopbMyduQsgHL4pepLps6g87icR4OHmpGnlTyRgeOhn1zGp/oy0zLNKtDXvbRxo+X/X5SuasOef795B/dZn5nRJ0LAx91uEOHDmH27Nlqr5yEOxkVEBUVpdr4y/4xaewhQbC99vA1xSWXXKLC6KxZs1RQlaqZjDyo29FSgpzMnJOmLxLgpFOkVBnfe+89tYdN9ug15Zim2LVrF7788kuL91144YVwdXVVy0Rl2ewTTzyhwp748MMPsWPHDlVZ3LdvH3x8fJr1PRJ1Z3tXJdULe1HD/NXgdHdfp1Z5DfnQRS3HjI42u92xb184DhwI3YEDgL093KafA8+LL4HLuLEt6phM1BJ6g141XFl6cinWJK5BaaX53NZqrvauKuSdF3EeRgeNhr1tE/9tkQ8hJdRJuJOQJ2Gv0nKQVGxsgZARQNRUU8CTr9lohboZBj4LZG+SBAxpL1+3pT21LtlHNnPmTBVsZOlg7Xa00v7/s88+wyOPPGJ13RxlILq8CZMAJTPnpHJ4+eWXq9EE/WvtkZEh6jLjTgaSy1684uLimnAnYUr2xcmIgjMd0xTffPONulgiM/2kOid78caNG6dGT1STyqI8btKkSer8ZZRCc75Hou5s1JwIHN2egYqySrVPb+JlvRHc29SsqKUM+fko+O13tTdPmq5E/PILHPuYhz6fm29WSzs9LpgHOwvNpYjaQ6WxEtvTt6tK3l+Jf6mRCg01XpE9ebN6zsL4kPFNX66pKzB105SQJ8PPC043JLLIPQTodQ4QdY6p2YpT6/xOEnVWnMNnwdtvv63eaMsb79YOfC2Zw0dEZ4e/T9QaclKK4RNSfx/coU2p6rrv2CDYtnCfnvyTXLp9B/K//x5FK1agqqKi5j6vaxYi8IknWvT8RK3FWGXEnsw9+DP+TzVGIVeX2+CcPBmdIJU8uZbQd0by1jTjIBC3HIhbCSRtb7zZisYB6DEO6DXddPHrw2Wa1GkUcg5f+5O9Y7LcTSog0qGQiIi6t5KCcrVPTyp5598xCD0H+Zrd339806rwZ6rm5f/0M/IXL0ZFQoLFYyqOn2jx6xC11PH84/jjxB8q6KUUp1g8xs7WDuOCx+G8nuepBiyu2iY0DKooNTVZkZB3dAVQmNz48T69TwW8c4Ae4wHtWYxpIOomGPjquPPOO9UsMVnCxsBHRNR9GQxG7FudjB2/x0NfbqoubPjuKEL7ecHOvuWjRqSap9u7F3nfLEbh0qVm1bxqtm5u8Jg7F56XXgLHRjoWE7WlzNJMLI1fqoLe4dzDFo+xtbFVDVekkid786QRyxnlJQBxK4Cjy4GTGxrfi6d1My3PrF6q6dWjBd8RUffCwFfLDz/8gN9++00No+bcMCKi7ivpSC42LD6KvHTzhhMaew1K8svh4dc61YTUJ59ExbHj9W53HjECnpddCrcZM2Dr2MzZY0StoLiiWO3H+/3E79ieth1VsLwDaKj/UMyOmK06bPo4mZp+NchQCSRtO13Fy7IcHmv49wd6zwB6nwuEjZZfwBZ8R0TdFwPfKbJuVhpa3HrrrRgzZkyrPa8036jN0rBqIiKyDkW5Omz64RiO78o0u93eUaMatMRMDYVGc3YdMKsMBtjU+jBRxp54Xb4AGadmYNq6u6txCl4LFsAhMrKF3wnR2XXY3JiyUYW8dcnrUG4ot3hcpEckzo88XwW9ULfQxp+0JAc4ttJUxTu+ytSApSEyWD1ikinkRc8EPMNb+B0RUZcLfPn5+XjjjTeafLwEPBl2LaQTpISxF198sQ3PkIiIrJFBb8SeVYnY+edJVFaYfzAnQ9PHXhQFFw+HZj+vsbwcRcuXq2WbziNHwv+B+83ul+6acr/H/Plwnz0Ltk6tM8qBqLnNV2S55vKE5SgotxzI/Jz8MCtiFuZEzkFf777qA4sGZccBR/4AYpeaKnoNVAcVj7DTAa/nRO7FI2oDXS7w1W43fyYyd0wC34YNG9Qssi+++MJs+HVrSEtLs9iJh4iIrENpYQWW/OdvFGSWmd0uHTknXRGN4F7N/3dBGq/kffsdCpYsUQ1Z1G2JifC7607YaE+3ote4u6PHl1+0wndB1DwJhQn45dgvKuillpg6zdblYu+C6eHTVTVvVOAoaGwb2O5iNADJO4HYP4AjfwI5cY3PxZPlmdUhT5ZtsqMmUZvqUoGvZ8+eahN8c911110YPHgwRo8ejWPHjpndV1paqm6TIOjra96ZjYiIOj8nN3u4ejnWBD4HZzuMnheJARODYduM5ZtVRiNKNmxA7pdfqeu6DDk5KN64EW7TprXq+RM1VWFFIZafXK6C3t6svRaPsbOxw4SQCTg/6nxMCZ0CR1lmaYm+DDix1lTJO7oMKMlq+IVlDl6vc00BL2oa4MyZkUTtqUsFvrOVkJCg9vD17t273n1r1qxRt0v3TpnP1144HpGo9fD3iRojS9MmXR6Nb5/fjj5jAjHmgig4uzdxILQEuaIiVcnL/fpr6BMS6x9gbw/3GTPgdcUCOA0f3ronT9SEoehbUrfg1+O/YnXialQY63eDrW6+Iss1Z/SYAU/HBqraJdmmcCdVPBmAXmleFTfjHQX0nQ30mW2q6DVUHSSiNsfAB+Dzzz9HhYV22JdeeimGDx+Oxx57DL169eqQN6mNrpEnojNi2KPaUo/lIyuhCIPPCTO73TvYBQv/NVZV+pqjeN06JN//AKpKzbt5CvvQUHhefhk8L7oIdj5n6F5I1MqO5R1TIU8asGSVWa6+hbuFY27UXBX0Gmy+knP81H68P0378aoaaj5nA4SOPB3yfKO5VJPISjDwAZg3b16D9wUGBuKSSy5Be3NwcIBOp4MTN/ATtYj8HsnvE3VvZcUV2LLkOA5vToONrQ1C+njBN9R8GHRzw55w7N8fVXq92W0u48fD6+qr4DppkllXTqK2lq/LVwPRfzn+Cw7lHLJ4jKu9K2b2nIkLel2AIX5D6n+wLB+Spe8HDv8KHP4NyDrS8AvKcs/IKaaAF30e4BbQyt8RkfV+mFxeaURZhQElFZXqurTO1zq9QR0jl9NfG1Cur31tRFFRI51rWwkDn5Vyd3dHRkaG+trR0ZGVPqKzGWqt06lmTgEBfBPSnf8/OLIlDZt/PA5diSmYVRmrsO7rI7jo4eFN/rtVGq8U/PmnGplgY3t6X5+dnx/cZ85E8erVqtOm11VXcqQCtSu9UY8NyRtUNU9GKcgSTktD0ccGjVUhb2rY1Pr78mRkVMrO0yEv72TDL+jsYwp3fWaZ9uNpXdrguyJqGzq9AUW6ShTp9Cg8dV1U67r2bSXllSq4lVZUX5/6utyAUr0BBmPrrCAyltdfIdLaGPisdCmYra2tepMqXT3lIhj6iJr3uyuVPfk9kt8n6n5yUoux7utYpB0z//TUM8BZNWVpyt+puthY5H35JQp++x1VOh20ISFwnTzZ7Bj/hx9C4LPPQONqXjEkakvH849jSdwStWQzV5dr8ZgojyjM6zVPLdn0d/avPwQ9cbMp4B3+HSiy3KlT8Y40VfH6ns/9eNThKg1GFJTpkVeqR0FZBfJK5OuKU7dVmG4v1SO/rAKFZbVDXSUqDN1zHjYDnxWTN6mtPSaCiKir05cbsPPPeOxZmQRjrU9gNXa2GDG7B4ae2wMae9tGB6QXr1mD3E8/Q+nOnWb3SQfOuoHPnhVkaicl+hIsi1+GJceWYF/WPovHeDh4YFbPWaqaN8BngPkHG5UVQPw64NAvpj15pTkNv1hgDNDvAqDfHMCvL/fjUZt9QCuVs5ziCmSXlJuui+W6HNnFFbUCnOla/izBzRo52Wvg4qCBk1YDZ3s7ONrbwsFeAwc7WzjYaUx/ttPAQV3bwvHUfVLhe7DpY8TPCgMfERF1GSf3Z2P9N0dRlKszuz28v7eaqefh1/BQZ0Nxianb5hdfQJ+UVO9+Wzc3OPTqpcYv1F7WSdTWb4hlhIJU85adXIYyC50xNTYaTAyZqELepNBJ0GpqdZmtKAWOrwIO/WrqsFluWjVkkTRd6TfPFPKkqkd0lv/PSmUto0iHzMJy5JSYwlt1kDOFu4pToa4cOn3HVN0c7W3h5mgPN0c7uNe5louLgx1ctHZwdtDAWUKcfF3rWu5T4U6rUWHP1vbsPhSRlXwPom0x8BERUZdwdEc6Vi4yb1Th7KHFxMuiETXMr8ElnPrUVFW5y//+exiLiurdr+0VBe+rr4bH3LmwdeF+JWofOWU5+O34b6qaF18Qb/GYCI8IXNTrIsyJmgNfJ1/zkHdsJXBgCRC3AtCXNjwEvcd4U8iT5ZoeIW303VCXCXK6SmQW6pBZVI6MQh0yCk3XWdV/LjLdVlHZPiHOzcEOni728HTSwtPZHp7OWniduvZwslcXt1MB7nSYM13bN2POamfHwEdERF1C5BA/tT8vP6NUrT6LmRKq9uppnRr/py7tqadRsmlTvdtdp0yB97XXwHnMGO6hpnYhDVc2p25W1bx1SetQWVV/6ZqTnRPO63keLup9EQb7DT79/6ZeBxz7Czi4BIhdBuhLLL+IrT0QOfl0yHOpFRSpW4e5nJIKpOXrkJJfhrQCueiQml+mglx1wGvLapxUynxdHeDjqoWPiwN8XbXwcjkd4LycTaGudqDrTqGtJRj4iIioS7Cz12DKVX2w6Ydj6tq/h3uTHue18OqawGfj6AiP+RfCe+E1cIiMaOMzJjJJKkrCT3E/qXEKmaWZFo8Z5DcIF/e+WI1UcLE/VWmuLDcNQD/4k2kYekX9CrUiXTl7TTeFvOiZgBP7A3Q30rgkNV+HVAlycp1fVvO1hLvUAl2rV+XkswgfF60KcbWDnFxLmDPd5qCOkdtkqSS1DZsqTiVuV7JO18PDAwUFBWr0AhERNY/BYMSelYlwctOi//jgevfLP2t1K3KG4mLk//ADDHn58L//PvPjjUYk3nAjXMaOhedll8LOy6vNvwcivUGPVUmr8EPsD9iWvs3iMV4OXmowulTzojyjzBuvyHJNGYheXtBwyOs9Axgw33TtwC6yXVlxeSWS80qRlFuGpNxSJJ36Wm5LyStDUXnrNjqRwObn5ogAdwcEnLr2c3dEgJsDAuTa3VGFOFbgrCMbMEoTEVGnkRFfiDVfHkFOSjG0jhr0GOADF08Hs2Nqh72K5BTkffGFCnvGkhLA3l7NyrP3P92iXhqw9Pj0k3b9Pqh7V/N+OPoDfj72s8VxCjIzb1zwOBXypoROgb3GHjDogWOrTJU8GaOgy7f85NKspde5wMCLTJU8B7e2/4ao3ebHyVJLCXPJeWUq0CVLuFPBrlR1sGwN9hobBHo4IsjDCcEejgj2dFJ/9q8Od+6OqjKntWOQ60wY+IiIyOpV6Cqx/dd47FuThOp1KRU6A7b+fBznXNe/3vFlBw8id9HHKFy+XEqCp+/Q65H3zTfwv/fedjx76u5kOLrsyfv+6Pdqj54lIa4huLDXheoS6BJoGoaeuAXY/71pjEJZbsN78nqdY6rkyTB0R4+2/WaozZRVGHAypwQJOSWIzy49dS1/LkV6oXnn4bMhn4X5uTogyNMJIZ6mUBfk4YgQTyd1mwQ8CXNn222SrBcDHxERWbWEAzlqgHrdUQvRowIw7uJeZks5SzZvRu6iRSjZvKXe89g4O8Nz/nx4Xnhhu5w3UWpxak01L6ssq979djZ2mBo+FZdEX4IxQWNgCxsg4wCw+V1g/49AYbLlJ7a1AyKnAAMuAvrOBpy4DLmzKK2oVAHuZHYJTtZcmy7S3bKlgS7I3RGh3s4I9XJCmJczwk59LaFOqnOszHVPDHxERGSVdMV6bPj+KI5uyzC73c3bEZOv6qOWc4qqykoULluOnEWLUH74cL3nsQsIgPc1C+F5ySXQeLD6QW3faXND8gZVzduYshFVqLJYzZMGLPN7zzeNU8g7CWx4zVTNyzpi+YltNEDEJFMlr99cwNm77b8ZOisGY5XaN3c8q7jWpUSFO+l22dK9c6G1gpwp1JmuZfklAx1ZwsBHRERW5/juTKz75ijKCivMPr0eNC0Mo+ZGQOt4+p+vqooKZPzznzAUmDevcOjdC9433giP2bNho601iJqoDaSXpKtxCnLJKDX/kKJ6OLoMRb+sz2UYGzQWmrI8YP9PppCXZLlpixI+Doi5GOh/IUcoWGG17kRWSU2gU9eZxWoZZvlZdryUv+ekGtfTxwU9fJwR4SvXpq8l4LGTJZ0N/l9DRERWZe+qJGz8Ps7sNp8QV0xd2BcBPet3MLN1dobXVVch+9131Z+dR46Ez003wmXSJM7PozZlrDJiS+oWLI5djPXJ69Wf6wpwDsDF0RerAekBdi5A7J/A2gWmJixVtfaXmj1oIBBzCTDwEsAzrO2/EWpUbkkFjmYU4Vjm6WqdBDtponI2ZIucVONMYc5ZhTt18XVR1ToHO02rfw/UvTHwERGRVek9MgA7/zwJXYkethobjJjdE8PO6wFDchLSnn0NTkOG1NuH53X1Vag4eRLe110Lp0GDOuzcqXsoKC/AL8d+wbex3yKxKLHe/TawwcTQibgs+jJMCBwNjYxRWPqEKezpSy0/qUe4KeTFXAoE1G9ERO0zqy4usxhH04sQm1GkQl5sejGyi8vPevllpJ8rotTFpaZax1BH7Y1z+NoZ5/AREZ3Z0R3p2LMyCedc2w/OuSeR89FHKFqxQjqzQNuzJyL//EONUyBqT7G5sfjmyDf4M/5PlFXWr+74OfmpcQoX97oIQUWZwN7FpiWbpdmWn9DZx7QnT0Je2GjTej5qlxEHUq1TgU6CXbqEu7Or2Em1TkKcBDpTsHNFlL8p4Hk6cyk5nRnn8BERUZclnzce3ZYOFy9HhPYx7zLYa7g/gioTkPfUvcjcbN7GXip5xatXw2369HY+Y+quA9L/SvxLBb3dmbstHiMdNhf0WYBJHtGwP7gE+OJiIPOQ5Se0dwH6nm8KeVFTAZmzR232d4zMrDuYWojDaYU4kl6ogp2MOzA2s9zhrNWoMNfrVJgzXbsi3MeZ1Tqyegx8RETU7orzdFj7dSwS9ufAzccRC54apRqxyBu04nXrkPO/D1C2u/6ba7vgIPhcdz1cxo7tkPOm7iOjJEN12pSxCjm6nHr3u9q7Yl7UPFwedQEiUw8C698BTqwBLOzjU2MUos4BBl1mmpWndWmfb6IbVu0OpRbiUJrpIiGvSFfZrOfRamwR6eeCPoFuiA5wQx+5BLqpRiqcT0fdLvCtXLlSXdavX4/ExERkZ2fDyckJ/v7+GDJkCKZNm4Z58+YhJCSkdc+YiIg6LQl0hzenYdP3cWpwuijK0akB6kM8TiD7fx+g/Ej9tvQO0dHwufkmuJ93HmzsWRGhtvv/c2fGTlXNW524GgYLTVV6efbCFX0uxxx7PzgfWAKsmgFUFFl+wsBBwJArTc1XXP3a/hvoJnKKy3E4rQiH0gpM16mFOJZVrMYhNJVkN2mSIoFOBbtTAa+njzPsNFwuTt14D19paSneeust/O9//1Mhr/qhjo6O8Pb2RllZGfLz82tut7Ozw9y5c/HQQw9hLD+NVbiHj4i6q5L8cqz+4ggSD5pXS4J6eWD8eAfkXn9pvcc4Dh4E31tvg+vUKey4SW2mVF+K347/prptHss/ZnGkwjnh52BB8CSMSD4Am32Lgfz6zVoU10BTJW/wAiBgQNuffBeXUajDvuQC7E8pwP7kfFW5a+6Acj83B/QLcke/IDf0PRXsZDmmoz2XYlLHs6o9fB9//DGeeuoppKWloV+/fnjmmWcwfvx4jBw50uzkJOzFxsZi69atWL58OX799Vf8/PPPuOSSS/DKK68gPDy8Tb4RIiKyTvLvQtyODKxffBTlpaeXV9k5aDD2wijETA6Bja0NyidORMmGDeo+l3Fj4XPLrXAePYpBj9pManGqqub9GPcjiixU6WQo+qWR83CJwQH+B34G1n5k+YnsHIG+c4AhVwARUwANd8ycjcxCnQp2EvAOyHVKAbKaMahcqnbSFbO/Cnfu6B9sCnn+bo5tet5EXabCp9FocPHFF+PRRx/F8OHDm/wCxcXF+Pzzz/Hiiy/i5ptvxtNPP43ujBU+IupOyooqsO7rWBzfnWV2e1CYA6bfOgzuvk41t5Xu3ImcTz6F7y03w2nw4A44W+oO5G3Prsxd+PLQl1idtNri7Lxh/sNwhe9wnJN8GPaHfgH0JZafrMd4UyWv/wWAo0fbn3wXIkFOhTpVvctXQa85lTsXreZU1c4U7CTkSeXOScuqHXUuhe2QDZoc+Pbt24dBLZhtVF5ejoSEBERHR6M7Y+Ajou4i+UguViw6iLIifc1ttkY9oo7/jP7D3BD6yssden7UvVQYKrA0fim+OvwVDucerne/g8YBc8LOwRWVWvQ59CeQU39pp+LVExh8pWnZpndE2594F1BcXol9yfnYk5SPPYmmcJdWoGvy472c7TEwxAMxIR7qWsJduLczm6hQl1BoTUs6WxL2hIODQ7cPe0RE3YmTm9ZsCad74Un0O/wZXMoyUZSuQcU9d0MbFtah50hdX3ZZNr6L/U4NSc/V5da739/ZH1f4DMMlafHwXPshYKFRixqlIPPyhl4NhI/hvLxGSOMU6Za5JykPuxNNIU/m3TW1n4qns31NsBt06jrUy4lLu4la4KwXmb/66qt48MEHW/LaRETURVVmZ6Pyy48REZ+BE6EzEXHyT4QnrYRtlRE2Tk7wuuwy2DqdXs5J1NoO5RxS1Typ6umNp6vM1QZ79sHVcMM5RzfC/uBOy08iw9Al5EnYc3Br+5PuhDKLdKpqJ8Fu96nqnVT0msLd0Q6DQj1N4S7UVMFjuCOyosD38MMPIzc3F88//3zrnhEREXU6+nKDquY56AuQu+hj5H37Lap0OoTDBr7pf8OlNAO2rq7wWng1vK+5BnZe5oPWiVpDpbESa5LWqP15sk+vLjsbDWa4R+PqnEzE7F5p+Ulc/IDBV5iCnl+ftj/pTkRvMKoRCDsT8rArMU8FvZT8siY91sHOVgW6IWGeGCyXUE+EeTPcEVl14Js/fz5eeukl5OTk4P33329w398jjzyCZcuWteQciYjIiqUdL8CqTw9Ba1OOmF/uB8pP782xQRXc7Mrgfddd8L5mITTcu0xtoERfgiVxS1TQSy1JrXe/l70bLtF4Y0H8HvifiK//BDYaoPcMYNhC07WGsx5FoU6PXQl5+DshDztO5mJvUgHK9BaWvFogw8sl3A2VS7iXmnNnz/l2RJ0r8H3//fe47bbb8MEHHyAvLw9fffWVmrsnkpOT8eSTT6rbjMb63a+IiKjzM1Qasf23eOxekYDq9l+Jkech/PDP6mtbDw94X3sNvBcuhMaNy+Go9WWUZOCrI1/hh9gfUKSvP1aht4Mvri4uw+yTh+BoqUedTy9TJU8qem6B6M6kh19yXhl2JuRi50lTyIvNKKr53T7TvjtTuPPCkHBPDAn1hIczQzNRpw98tra2Kuz5+fmpkQvSWWbRokX473//qy4yhH3AgAFc8klE1AVl7o3Hmt8ykZ1s3q7ebshoaFLXwPv66+F19VXQuLp22DlS1xWbG4vPDn6m9udVVpnvF7OBDSbb+2Bh6gmMLE5EvQWD9s7AgIu6fQOWSlmemVZYE+6kgpfZhJl30hizb6A7RvT0wtBwU8jr4ePMpZlEVqzJYxka8/rrr6sGLhIC5el69uyJ5557DldddRX/AqiDYxmIqDMrT0rGjjd/x/6CnjBqtDW3u3o74Jxr+yO4hxOqKg3QuLp06HlS1yPvLzalblJBb2va1nr3O9rY4YJKDRamxqNHpYWmIYExwPDrgZhLAUf3brn/ThqqbD2Rg20nclXIa0pzFWetBsPCvTC8h5cKeVLJc3Nk9Y6oS45laMh3332H//3vf+prWb4ZGBiIHTt2wNvbuzXOj4iIrIA+LQ3J7yzCtqMeyPEeANSabdxndCAmLoiGg1OL/0khsjg/748Tf+DzQ5/jWH792Xg+NlpcUVCAy/Ny4Fl3G4lU8wZebAp6IcO6VTWvvNKghppvk4AXbwp4pRVn3n8X5OFoCncq4Hmjb6Ab7Lj3jqhTO+t/nZcuXar26e3Zswf29va477771PJOuW3GjBmqUYuvr2/rni0REbUrfUYmcv73P8StOoTDUQug9z69F8/OUIaJc4PRf17/Dj1H6poKygvU/Lyvj3ytZunVFVmlwbXZmTi/pAQOddcqBQwEhl9nGo7u6IHuQKc3qNEIUr3bFp+jumjq9I33UbCpXp55qnonAS/Ek+NSiLqasw58559/vlquefnll+OFF15QyzhFUFAQbr75ZkyYMAErVqxAeHh4a54vERG1g8qcHOR88CHyFi9GkZ0P9o38h9n9AR46zLh/AtwDu8ebaWo/yUXJqpr387GfUVZZv+X/6PJKXJOXiwllOpjVneycTNW8EVLNG97lq3kVlUbsTszD5uM5apnm7qR8dduZ9t/JzLsxkT4YHeGtAp6HE5dnEnV1Zx34pk6dipdffhnDhw83u/26665TyzkXLFiA8ePHY/ny5ejfn5/+EhF1Bob8fOQsWoTcL79CVZnpzbZreSqCUzciNXgCbG2qMHpeTwydGQkbefdI1IqNWBYdWITlJ5fDWGWs92ZlZnEJri0oRL+KOkPU/QeYQp7szXPyRFdlNFapJiubj2dj47Ec7IjPPeOIBI2tjRpoPjrCB6MjvVUlj/vviLqfVmnaYsm6deswb948tdwzO7v+Uozuik1biMia6WJjEX/BhWa3aTw84H79TdhVORyjLuwF31COWKDWIW9B/s74WwW9jSkb693vaqzCJYVFuKqwCIGGWuHGztHUaVOCXujILlnNk59NQk4pNh3PxqZj2dhyPAd5pXXCbh32Ghs10FzCnYQ82Yvn4sC9tUTWrFM0bWnI5MmTsXbtWsyaNautXoKIiFpZhU840qfficC/3oGtmxu8r78O3tdco8YrzO7ok6MuQyp4a5PWqqC3L2tfvfsDKg1YWFCIi4uK4Vr7c2mvnsDIm4AhVwHOXa85XGaRTgW7jXHZaqlmSn79Ja212dnaqK6Z46Kkguejumk6aWt1VCIiasvAJ4YOHYpNmza15UsQEdFZMJaWIverr+A6aRIc+/RRt8VuS8e6b2Khr+wPj6sexeB7L4KGKxGoFekNevwR/wc+OfAJThScqHd/RIUeNxQU4vziEpxeeGgD9J4BjLoZiDpHBgGjKzVakf1364/KMs0sHM0oPuNjpGvm+F6+mNDLFyMjvOHKCh4RnUGT/5bQ6XRwdHREc0VFRbX4OYiIqHUYy8uRv3gxsj/4EIacHJTt2YuAV9/A+sVHEbs1vea4XXmR6GfjCPbro9ZQqi/Fj3E/qhl6GaUZ9e4fpCtXQW9qadnpRixOXsDQhcCIGwDvCHSVZZpxmcVYfzQL645mqXEJZ2q0EubthPFRvirkjY3yga+rQ7udLxF1s8AnwU1GLtx0001qX15zHDx4EE8//TSGDBmCp5566mzOk4iIWqCqshIFP/+MrHfeRWVaWs3tqduOYu0zm1BYcHp/lJ2DBuMu7gVHVzZ3oJbJ0+WpsQrfHPlGjVmoa3xpGW4sKMQIXbnU8UyChwIjbwYGXgTYd/6PHApK9dh4LFuFvPVxWUgr0DV6vI+LVgU7CXgS9MJ9nNvtXImomwc+6cp51113qeAmHTgvu+wyjBo1Cg4Olj9pSkhIUGMZPv/8c2zevBkhISG49957W/PciYjoDKqMRhStWIGsN99CRXz86dthg+Qe03EsYh6qaoU93zBXzLhxALwCXTrojKkrSC9Jx6cHP8WPR3+EzmAecGyrqjCzpFRV9PpWd9zUaE0jFSTohZp3/+5sDMYq7EvOVxU8CXkyG8/YSHs8rZ2tGpEwqbcfJvT2RZ8AN9iyAy4RdVSXzu3bt+Mf//gHVq9erf5sZ2eHfv36ITAwEF5eXmrJZm5uLmJjY5GVlaWWLsgw9nvuuQcPPPAAnJw6/yd1LcUunUTUHuTv35KNm5D1+uvQHTpkdl+FgwfiJj2IDL2P2e2DzwnD2AujoLHvOnukqH0lFSapRiy/HP8FlcZKs/scjEZceGq0QljlqQ8ZPMJNnTaHXQO4+KKzyi2pwLqjmVh1OFNV8/LP0E2zl7+rCniTon1VN002WiHqvgrbIRuc1ViGuLg4LFq0CKtWrcKePXtgqN0qGVAhb9KkSbjoootwySWXNHsJaFfGwEdEbU2fkYHUhx9B6fbt9e4rO/dq7NFOQFnJ6b+3ndzsMe2afugZ03nfcFPHOpF/Ah/t/wh/xv8JQ5X5ewI3gxELiopwZUERfI2n9qv1mACMuQ3oMxuw7XxhR946HU4rwppYCXkZauh5Y++m3BztVJOVSdES8vwQ4skPwInIyscy9O7dGy+99JL6urS0FCkpKcjJyVEVPH9/fwQFBbX2eRIRURNpvLygT042u81lwgT43ncvVqysQFlsfs3toX29MP36/nDxYCMIOrth6R/s+wArE1aiCuaJx9tgUNW8ywpPjVbQOABDLgVG3woEDUJnU1ZhUEPPVx3JxJojmY3uxZOxgINCPFS4mxztp0Yn2GlYOSeiLjZ4nSxjhY+I2kP+Tz8j7fHH4TRkCPweuB8uo0ap24vzdFj8r+3Qlxkw+oJIDD03HDbcL0TNJLPzPtz3IdYmr613X0BlJa5XM/RK4ChvMVwDTLPzhl8PuPqhM0nOK1XhbvWRTDUXr7yRjpqezvaYEu2HqX39MbG3H7xdtO16rkTUORVaa4WvMe+++y5uvvlmLuMkImpj+sxM5Lz/PrQRkfBeeLXZfR7z5sLOzw8u48fBRsoNp7h6OWLGDQPg4GyPgAh+6ETNszN9p6robUnbUu++EH0lbioowLyiEmiru22OuQPofyFg1znCj9FYhb3J+VhxKAOrD2ciNqPojDPxpvX1V5eh4V7Q8MMTIrJCrR747r77brWkc8KECWovX21btmzB2LFjW/sliYi6FUNhIXI+/Ai5X3yBKp0OGk9PeMy/EBpX15pjKiuBHQk+GNijGH5hbmaPDx9g3qyFqDGyEGhL6hb8b9//sCtzV737e1bocUtBAWYVl8LORgMMmA+Mvh0IG2Va22jlyisN2HI8R4W8vw5lILOovMFjHexs1biEqadCHvfiEVG3DHzyD8Oll16qrmU/X0xMDAYNGoSePXvimWeeUXv9iIjo7Iam5331NbL/9z8YC07PNDPk5yPv62/ge8vN6s+5qSVY9uEB5KWVICU2D5c9PhJap1b/6566OPl3fG3SWlXRO5BzoN790eUVuCW/ANNLy6CRIekTbjUt3fQIhbUrKNNjbWymCnnrYrNQXG7eUbS2YA9HTOtnCnhjI33ZUZOIOp02eQdw4sQJNZZh37592Lt3L/7++2989913GDNmTFu8HBFRl5+lV/j778h6403oU1PN7rN1d4fPzTfB+2rTks4jW9Kw7ptYVFaY9hoVZJZh1/IEjLkwqkPOnTpv0Htv73s4nHu43v0Dy8txa14hJpeVwcY7Eph6JzD4SkBr3QPC0wrKVAVPQp5U9CobGY4nTVbO7R+Ac/r5q7l4tZdFExF1Nm0S+BwdHTF8+HB1ISKis1e8aRMy//Mqyg+bv/G2cXSE98KF8LnpRmg8PKAvN2DNZ4dwZEu62XEDJ4dgxPk92/msqbMGvXXJ6/DunnctBr1hOp0KemN1OtiEjwXG3gX0mWW1YxXk+4nLLMaKg+kq5O1LPl0Vr8teY4NxUb4q5MklwN2xXc+ViKjTBb7Dhw+rQexs3EJEdHaqKiuRdPsdKNmwwfwOW1t4XDQffnffDfuAAHVTTmoxln94UC3hrGbvqMG0hf3Qa7h/e586dTISjNYnr8e7e9/FoZxD9e4fXabDbfkFGFGuB/rNA8bdDYSOgDXPx1t6IA1/7k/D8azTvxN1uTnYqb14MwYEqNEJbo58z0JEXVObBL5p06bBzs4O0dHRNXv4qi+hoda/tp+IqKPZ2NnBztd8ELrr1Knwf+B+OPTuXXPb4c1pWC9LOPWn28X7hbthxk0D4Olv3UvsqOPD0YaUDaqidzDnoMWgd0deAYYZ7YCh1wFjbge8I2CN38eBlEL8eSANS/en4WROaYPHBro7qgqehLzRET7Q2nE2HhF1fa0+h69///748ccfkZ6ejgMHDqh9fHI5ePAgysrKYDAY0J1xDh8RNZU+LQ3Hz5sFh759EPDQQ3AeOfL0feUGFfSObDVfwhkzJRTjL+4FjT3fyFLjQe+9Pe/iQANB7/a8Agy39wJG32Kan+fsDWsbn7AnOV8FvKUH0pGcV9bgsb39XTFzQKAKeTEhHtyPR0TdLhucdeCbPXs2XnvtNfTt27dJx8vLHD9+HL169UJ3xsBHRLUZdTo1XqH88BGEvPZqvfvL4+Kg7dWr3pvU9PgCLHllF6pONZ7QOmowlUs46Qz/Dm9M2Yj39ryD/Q0EPbV00z3KtD8v5hLAzgHWFPL+TsxTSzWXHUhHWoGuwWP7Bblj9sBAzIoJRC9/87EkRETWxKoDn0ajURcZsv7cc8/Bt87SI7KMgY+IRJXBgIKff0HWf/+LynRTlS78k4/h0oxZpdJ9c8tPx9USzpk3D4CHH5dwUn3yz/ym1E14b9d/sS+3/h69UaeC3sjAUcD4+4Be51jN/DwJebsS8/Db3lRVyWtsRp5U7yTgzRoYhAhfl3Y9TyKiLhn4ZJnmQw89hL/++kud3BNPPIH77rsPWq229c+yC2HgI+re5K/ckvXrTZ034+LM7nMeMQI9vvyi6c9lrMLBDSnoNy6YSzjJom1p2/Dfna9hr4WgN1KWbuYXYmTP6cCE+62mEUv1nrzf9qXi972pSG2kkjc03BOzpJI3MAhh3vzAg4g6n0JrDnzVli5diocffhiHDh1Sw9VffPFFXH755a13hl0MAx9R96U7cgSZL7+Mks1bzO/QaOB52aXwu+MO2Pn51XtcfkYpDmxIwfiLesHG1joqL2Td9mTuwdvbX8a2nP317htepsOdBSUY2Wc+MP5ewC8a1iAuo0hV8n7bl4b4bMvdNaXwOKKHlwp45w0MRLCnU7ufJxFRtwt8wmg04qOPPsKzzz6LjIwMNWD91Vdf5aB1Cxj4iLoffUYmst56EwVLfpLyhdl9bueeC7/774dDpOXuh8d3Z2LVZ4eh1xkw5sJIDD+PM/WoYUdyj+C/W57H+uw9Fufo3VlUjlExC4ExdwAeIehoiTmlqpInQe9IelGDx0nImzMoCLNigjgjj4i6lMLOEviqlZSU4KWXXsLrr7+uOnJeeuml6s9S+SMTBj6i7iXvm2+Q8fIrqCoz7yLoNGQI/B95BM7Dhlp8nMFgxNafjmPPX0lm1Y0rnhkNr0DuTyJzJwpO4J1N/8SKrJ317ovRlePuUiPGDL0ZNqNu7vCOm+kFOvwuIW9fGvYm5Td43MAQd8wdFIzzBwUh1IvLNYmoayrsbIGvWmpqKv7xj3/giy++UHv6SksbnonT3TDwEXUvhX/+iZQHHqz5s31oKPwfehBuM2c22B6+pKAcyz88gLRjBTW32dnbYspVfdBnTFC7nDd1DkmFSXh/03P4PWMbjHX+d4our8BdFfaYMuIu2AxbCGg77oOCQp1ejVD4aXcKtsXn1i101+jl74p5g4NVNS/Sz7W9T5OIqEtmg1YbvJ6bm6tm7cnsPbnEx8ersFde3nBHLSKirs5t1iw4ffY5yk+cgO9tt8Fr4dWwbaS5VcrRPCz/6CDKCitqbvPwd8KsW2PgE8I3wGSSUZKODzY8gyXpm1EpQa9W2OtZocedRhfMGP0EbGW0gsa+Q85RbzBiXWyWCnkrD2egotJo8bgwbydVyZs7OBh9A904J4+IyFoC36JFi1Swqw55sndPVBcMbW1tERUVhcGDB7fe2RIRWSkJdNnvvofAp56ExsOj5nZ58xr00ovQeHrCzsurwcfL3527VyRi6y8nambricihfph2TT84OLXa53PUieWWZmPRhqfwbdpGlNcJesH6StwGd8wd/zjs+s6Tf4jb/fzk/+M9Sfkq5P2+Lw25Jac/uKjN380Bc1TIC8KQME+GPCKiNnTW7yBk/l41KUOOHz9ehbtBgwap64EDB8LZmWvuiahrq8zNRfbbbyPv2+9k453qshnw6CNmxzhEWG7IUq28VK8as8Tvza65Tbpxjp0fhSHTw/hmmFCoy8Nn6/6BL9M2oLRO0POtNOAWG09cPPEpaKPP65AZetJ8RULez3tSGuyw6epgp0YozB8agtGRPtCw4ywRkXUHvv/7v/+rCXg9evRo3bMiIrJyxvJy5H3xBbLf/x+MxcU1t+d++SW8rroK2tCmd0Dc/nu8Wdhz9tBi5k0DEdzbs9XPmzqX8opifLP2CXyYsgaFUrCrlZE8DAbcaOuDBZOfgVNU+w9Lzy+tUFW8n3enYGdCnsVj7GxtMDnaDxcODcG5/QPgaK9p13MkIqIWBL4nn3yydc+EiKgTkCVr0ogl69XXoE9NNbvPLiAAfvffB/vg5jVWGT03EokHc9W8PQl5M24aABcPh1Y+c+pMDBWl+HXdk3g3aSXSJSPVWp3pajTiGo0fFk55Bq6RU9v1vCplX97RLHy/Mxmrj2SiwmB5X97gUA9VyZN9eT6u/H+ZiKgjcVMIEVETle3di4wXXlTXtdk4O8P35pvgfd11sHVq/iBorZMdzrtlIOJ2ZGDU3AjYatp/7xVZhyq9Dms2PIe34n/DcTsboFZBzMFoxJX2Abhh8rPw7DmpXc/rWGaRCnlLdqcgq8hyM7ZQLycV8qSaF8UOm0REVoOBj4joDCqzspDxyiso/PU38ztsbeF58cXwu+dutXevKYpydSjJL0dg5OnGLkI6cLILZzdWWYFdm/6N1+O+xR57G0DC3imaqipcaO+P2yf8HwJ6TGjXUQq/703D938nYXei5Xl57o52OH9QMC4aFoLh4V6w5b48IiKrw8BHRHQGVQYDilb+ZXaby/jxanC6Y5/oJj+PGrnw4QE1g+yyJ0bCzduxDc6WOpXKCsRtfRNvHv4M67Q2gIS9Ws6188Fd459BZM/2WbppNFZh64kcfP93MpYeSINOX3/JpmQ62Zd36YgwTOvrz315RERWjoGPiOgM7AMD4XPTjcj+79vQRkQg4LFH4Tp5crP2/e1bnYxNPx6rGbmw9P39uPiR4dDYcflmt2TQI3X7e3hn/4f4TVuFKgl7tYzUeOC+Mf/AoF6z2uV0knJL8eOuZPzwdzKS88osHhPp64JLRoTi4mGhCHDnhxVERJ0FAx8RUS1l+w8AVUY4DRpkdrvPDTfAztsbnpdcAhv7pg+y1lcYsOaLI2p/XjVZ9tZ3bCBsNVz+1u0Y9Mjb+RE+3PMuFjtUQe9gPmOhj60z7hvxEMb3vaTNx3Ho9AYsP5iOb3ckYfPxnAZHKcwZFIRLR4RiWLgXR4QQEXVCDHxERBLMMjOR9fobKPjpJzhERyNiyY+wsTv9V6Q0Y/G64opmPWdhdhmW/m8/spNOj21wcrNXDVqCezc8hJ26IIMepbs/w5c738QnDkYUO5rPWAixccDdg2/HrEHXw9ambau+cRlF+GZ7EpbsTkZ+qd7iMWMivXHp8DDMigmEs5ZvFYiIOjP+LU5E6O7z9HI/+RTZH3yAqtJSdVv50aPI/+EHeC1YcNbPm3goBys+Oojy0sqa2wIi3HHeLTFw9WKb+m7DUAnD3q/x67b/4G2tHplO8s/u6UDnbWOHW/pdi8uG3Ql7TdMrx2dTzftjXxoW70jEjpOWZ+aFeDrh4uGhuGRYKMJ9nNvsXIiIqH0x8BFRtyT76oqWr0DmK69An5Jidp99j3DYh4Sc9fPuWp6Arb+cAEzb9ZT+E4Mx6bJoaOy5Z69bMBqBg0uwecO/8B/7MsQ5a83+yXWGLa7rfSmuGXk/XOxd2uw0jqQXYrFU83Ylo1B3+sOHalqNLWYMCMCCkeEYF+XDLptERF0QAx8RdTu6Q4fUPL3SnTvNbrd1dYXvHXfA++qrYKOVN+jNt+qzw4jdmn76Oe1sMHlBH/SfENzi86ZOQFqwxv6J2DX/h9eQg80uMpfx9P9LdrDBpT1m4tbRj8HHyadNTqG0ohK/70vDN9sTGxynEOnngitGhqtxChyMTkTUtTHwEVG3UZmdjcw33kDBj0tMb8xrz9O75BL43XsP7Hxa9ia8xwCfmsDn4umA824diMAI85l71AXJ/0/HVyNj9XN4pyIJP7u6oMpGwt5p0wNG4d6xT6GnR882OYWDqQUq5P2yOxVF5RaqeXa2mD0wEFeMCseoCG82YCEi6iYY+Iio20h99DGUbNpkdpvz6NEIePwxOPbt2yqv0XtkADITCpGZUISZNw+Es/vZVQqpE0ncipJVz+GTgoP4zMMNOgdXs7sHeUThwbFPY1jAsDbbm/fF1gTsSbJczevt76pCnlTzPNXSUiIi6k4Y+Iio25AKXnXgsw8Lg/8jD8Nt+vSzrnTITD3Zs2erMd+XN3Z+lNq+p6lzO3UxqXtQueqfWJK5Fe96eiDHy7ySG+Loi/tGP4qZPWa2ejVN5uZ9uS0B3+1IQp6FTpsOdrY4f1AQrhwVjuE9OE6BiKg7Y+Ajoi7JUFQEjZub2W0yW8/ryitgFxQE72uvhe1Z7tMTFbpKtV/PxcMBkxZEm91XNwBSF5N5BFWr/4UNCSvxqrcXTvh6m93tbueMW4bcgSv6XgGtpvUqakZjFdYdzcLnW05i7dEss1XJ1foGuqlq3oVDQuDh3HZdP4mIqPNg4COiLsVQXIKc999D3jeLEfHTEmjDw83uD3z66Ra/hszX+/O9fchJKVF/9g11ZVOW7iD3BLD23zh0ZAle9fbE9kB/s7vtbTS4ot9VuGXQLfBwaL19m3klFfhuZ5Kq6CXlllnstDk7JhBXj+nBah4REdXDwEdEXYIsrSz8/XdkvvIfVGZmqtsyXnwJYe+926qvkxybh+UfHICu5PQyuridGeg3PohvtLuqogxg/cvI2vM53vJwxS/BAaiq89/6vB4zcc/wexHmFtZqLyt78r7YkoDf9qWiotJocW7elaPDcfnIMPiy0yYRETWAgY+IOj3dkSNI/9e/ULbzb7Pby/bvR2VWFuz8/FolUO5fm4KN38epvXvVBk4KwYTLejPsdUW6QmDzW9BteQefO9vho2B/lNmaL9cd5jcUD458CIP8BrXOS+oN+HVvKr7cmoB9yQUWj5kU7YeFY3pgWl9/aDg3j4iIzoCBr5bS0lL85z//wbfffosTJ07AyckJffr0wSOPPIL58+d39OkRUR2G/HxkvfUW8hZ/axp0Xc3ODt4LF8L3zjugcXVt+evojVi3OBaHN6XV3CYDqicuiFaBj7qYynJg58eoWv8KltuU4bVAT6TZmf9zGe4WhgeGP4hp4dNaJeynFZSpap6MVbDUhMXd0Q6XjQjDVWN6IMK37Qa1ExFR18PAd0peXh7OOeccxMXF4frrr8cDDzyAkpISHD58GAkJCR19ekRUS5XBgPzvf0DWG2+o0Feby7ixCPjHP+AQFdUqr1VSUI5l/zuA9BOnqy1ObvY475YYBPf2bJXXICthNAD7vwfWPI8Dpen4t48X9jj6mh3iZu+KWwffhiv7Xgl7jX2Lq8a7EvPxyaZ4LD2QDkOtynG1gSHuuGZMT8wdHAwnraZFr0dERN0TA98p99xzD44dO4Zt27ahf//+HX06RNSAiqQkpNx7H3SHDpndbh8cDP/HHoXbuee22vJKmae39P39KM4rr7nNN8wVs28fBDdvx1Z5DbIC0u4ybiWw6jlkZB/GW14e+DUk0OwQW9ji0j6X4o4hd8Db0bwrZ3PJfrw/96epoLfXwrJNacIyZ1AQFo7tgSFhnlwuTERELcLAB+DkyZP4+uuvcd9996mwZzAYUFZWBtdWWApGRK3LztsblTk5NX+2cXCAz003weemG2Hr5NRqryPVl81LjpuFvV4j/DHtmn6wZ6Wl60jeCax8BmWJm9TQ9I9Dg+rt0xsTNAaPjHwEvb16t+ilsovL8fW2RLU/L7Po9P9X1fzcHNTePGnEwiYsRETUWhj4ACxbtgxGo1GFvYULF+K7775DRUUFQkJC8OCDD+L+++/v6FMkolNsXVzUwPTUBx+C6/RzEPDYY9CGhrb660hV5dwb+uP7F3eqZZ1jLojEsJk9WG3pKrKOAqv/D1WHf8OfLs54PTQIGXX26fV074mHRjyESaGTWvTf/WBqAT7ZdFI1Y7HUbXNwqAeuHx+B2TFB0NpxhiMREbUuBj4AsbGx6vrxxx+Hr68v3n//fWi1WnUte/ny8/Px3HPPndVzBwUFmf1ZgiURNU3xpk2oKiuD2/TpZre7z56tlnA6Dx3apq8vQ9Vn3RaDssIK9BxkvpeLOqnCVGDtS8DuL7FXq8HLQQHY52heTXPTuuH2wbdjQZ8FZ71PT/bjrTyUgY83xWN7fG69+6W75qyBgSroDQvnsk0iImo7XSrwSTB74403mrVvz9vbG0VFRerPUtXbsGEDfHx81J8vu+wyVfV7+eWX1XJPLy+vNjt3IjqtIjkFmf9+CUUr/4LGxwfOo0dD4+ZWc7+8OW7tsJefUQqDwQifYPOl3AE93Vv1daiDlBcBm94ENr+NzKoKvO7rid9dzbtdamw0uDTatE/Py/Hs/r4vKa/E9zuTsGhTvMUh6Z7O9rhyVLjanxfk0XpLkImIiLpN4GtOJe7qq69WgU/GL4g5c+bUhD1hb2+PK6+8Ev/3f/+HrVu3YtasWc0+p7S0023cRWFhITw8PJr9PETdgVGnQ86HHyHno49QVW7a42TIyUH2228j4PHH2+x1kw7nYvmHB+DgbIdLHxsJR9eWdV8kK2KoBHZ/Dqx5ARUlWfjCww3/8/Spt09vXPA4PDziYfTy6nVWL5NZqMOnm0/iq22JKCirP1ahT4Abrh/fExcMCWG3TSIialddKvD17NlTNVportBT+38CA827stVekiljG4iobcjvbdHKlch86d/Qp6aa3efQvx/cZp7XZq+9f20yNnxnGqZeXlqJZR8ewAX3DoENB1p3gc6bK4AVTwHZsVjv5Ih/hwYh0d6+3j69h0c+jIkhE89qWWVsehE+2nACv+xJRYXBfMm+PN05ff1xw/gIjI3y4bJNIiLqEF0q8J2tUaNGqevk5OR691Xf5u/v3+7nRdQdlB8/joznn0fJ5i1mt2s8POB3//3wvPQS2GhavyJiNBix8bs47F+XUnObrZ0N+o4NZNjr7NL2AiueBOLXI8HODi8H+GG9s/nySVd7V7VP74p+V8De1r75HVyP5+CD9Sew7mhWvfsd7GxxyfBQ3DghApF+7PZMREQdy6bqbEpiXYyMYYiKikJWVhaOHj2qunMKGbzer18/tcdPgp+Li/l+j7NRvaSzoKAA7u7cG0Tdl6G4GNlvv4PcL78EKitP32FrC68Fl8Pvnnug8Wybwea6Ej1WfHQASYfzzIapz7o1BkG9OEy90ypIBlb/C9i7GCU2wAee7vjcwx2VdSpr83vNxz3D7oGvU/Ma8egNRvy+LxUfro/HobTCevf7uGhxzdieuHpMOHw4VoGIiKwkG7DCJ5UEjQbvvvsu5s2bh7Fjx+KOO+5QXTo/+eQTJCUlYdGiRa0S9ojotMxX/oP8b781u81p+HAEPvkPOPbr12avK81Z/nh3n7qu5hPiooapu/uyiUanpCsENr0BbHkHVZU6/C5jFrw9kVVnzEKMbwweH/U4YvximvX0hTo9Fm9PVKMV0gp09e6P9HXBTRMjcdGwEDjac38eERFZF1b4alm3bp1q+rJjxw5V9Rs6dCgee+wxzJ07t9VegxU+IhN9ejqOz5qtxi7Y+fmp2Xruc+a06T6n5CO5WPbBAbVXr5qMW5B5e1pHfv7VKRuy7PoUWPMiUJqNQ1p7vOjjjT11xix4O3rj/uH3Y17UPNjaNH3OXUahDos2xqth6cXltarQp4yK8MYtEyMxra8/bLkMmIiIrDQbMPC1MwY+6o6MJSVqYHpdOYs+hiEvFz633Q5NnRb5re3A+hSsX3xUNWepNvTccIyZH8U3652N/LN1dBmw8mkg+yjybG3xlpcHfnRzRVWtDwzsbOxwVb+rcOvgW9VsvaaKzy7B/9Ydx5JdKfUascj/KjIg/eaJkRgcxuW/RETUMlzSSUSdWlVlJfK+/RZZb/0XoW+9BZfRpgZJ1XxuvKHdzkWvM9SEPVuNDaZc1Rf9xpm68FInkr4fWPY4cHIDpOb2nZsr3vbyRJGm/piFR0c9ikiPyCY/9f7kAry/7jj+PJCmMmVtzloNLh8Zpjpuhnk7t9Z3Q0RE1OYY+IioTZTu3In0f/4L5bGx6s/SiTNiyY+wqbOvqr0MOTcMueklOLkvG7Nui0Ewm7N0LsVZwOp/Ars+l48SsNPRAS/4eCFOqzU7LMQ1BI+MfARTw6Y2aXmwLHLZciIH7609jg1x2RYbsdwwIQJXj+4BD2fOZyQios6HgY+IWpU+IwOZL7+Cwj/+MLu9IjERusOH4RTTvIYZrUXe/E+5og9K50TAzduxQ86BzkJlObDtfWDdK0BFEbI1tnjV2xu/11kC7GTnhJtibsK1A66Fg+bMHTKNxiqsPJyBd9cex96k/Hr3h3g64dbJkbh0eBgHpRP9f3v3Ad5U9b8B/M1sOtO9Fx20rLL3XrJERZaiMhRBFBEURcC9t/4VRcW9/amI7L33plB2oYWWQveeSe7/ubfS9lJURps0yft5nlpybhJO4Bjy9owvEVk1Bj4iqhOm8nJkf/MtMj/9FEJx9QmYIteBA+H31JPQBAaapS8pJ3KQlVqIln1CZO0qjZJhz1qIayqPL62sp5eTJC3f/NWtcvlmoVK+fHNg+EA80e4J+Dv7/+fTlhtM+OtgqrR0MzGjqNb1xn4umNIrErfGBUJzxTJRIiIia8TAR0Q3rXDTJlx67XWUJyfL2h2io+A3dy6cO3UyW18StqRi888nYRIEKdxFtPIx2+9Ndb9PT3TQQYtXvDxxwkG+fDNSH4m5neaivX/7/3zK4nIDftl9Hl9sOYMLVymt0DbMAw/3ikTvGJ64SUREtoWBj4humLGgABeemoXCDRtk7UoXF/g8OhUeY8ZAoTHPvidxid72hadxaO35qrb13x1DcIwHtI58q7OafXobXqncpyeYkKVU4gNPdyxydam1fPPhlg/jnqb3QKP89/FVUFqB73YkS+UVsovKa13vHeODKb2ipBILREREtoifgojohomlFgxZWbI2/Z13wvfxGVB7e5utHxVlRqz5KgFnD1UfuqFz1mDQQ80Z9qxpn97md4CyfBgB/O7qgv+7yumbA8IHYGa7mf+5fDOvuAJfbz8rFUvPK6mQXRMn8Ia2DMTkHpFoGsjyOEREZNv4SYiIbphCqYT/M3ORNGo0dC1aSL92bNnSrH0oyi3Dsk/ikXGuoKrNw98JQx5pCb2Po1n7QjeyT2/Z3/v0zkpNh7VavOLtgaMO8oNXwt3CMafjHHQO7PyvTynO4n259Qy+3Z5cq1i6VqXEyHbBUtAL9WJpBSIisg8MfER0TUpPnkTpkQS43zlM1u4YF4ew77+DY9u2UgA0p4zzBVj2cbwU+i4LjvXAwEnN4cAj9Bu2SwnAillV+/RyxeWbHu5Y6OosK56uU+mkwuljm46FViXfw1dTekEpvthyFj/sTEZxuThHWE2nUWJMhzDp1E0/Nx7aQ0RE9oWBj4j+lTE/Hxnz5iHnx5+kQOfUtg20YWGy+zi1/+9DM+qaWE9v1ZcJMJRVf7hv2jUAPcbEQMXTFRuu4mxg4+vAni+kfXomAH+6OEt79XJV8vIHfUP7Ylb7WQhwCfjHp0vLK8Fnm87g593nUGYQn01eLP2+zmF4sHsEvF3+u1QDERGRLWLgI6KrEkwm5P35J9LffQ/G7OzKNqMRl15/AyGfzrdo345tT8OG749JKwIlCqDzsEi07h96TcW2yQJMxsrDWNa9BJRUjqejWg1e9fJEvE4exkJcQzC7w2x0D+7+j093PrsY8zcl4ve9KSg3yoOeq4Ma47uG4/6ujeDh/M+zgkRERPaAgY+IaimJj8fFV15FaXy8rF3t7w/9bUMhCIJFg5VfIzdodGqUlxig1ijR7/6miGzta7H+0H84twtY8SSQdki6WaBQ4CMPd/zi5iJbvikWTBeLp09oPuEfi6cnZRbh4w2n8eeBVBhMlxN/JXcnDR7o2ghju4RD78glvURERCIGPiKqIp64mf7ee8j7Y6GsXSyt4Hn//fCePAlKJ8sfduEZ4IyBk5tj/bfHMHByC/iF86TFBqngIrDmeSD+F+mmGM9WOTvhLU8PZKjlyzd7BvfErA6zpNm9qzmXVYyP1p/CwgOpMF4R9LyctXiwRwTu7RQGFwf+s0ZERFSTQhB/VE9mk5+fD71ej7y8PLi58UMqNQyCwYCcn35CxkfzYCqoPu1S5NKrF/xmP11r315DYKwwQaXhfr0Gx1AO7JoPbHoLKC+Ums6rVdLyzW1O8pNTg1yC8HSHp9ErpNc/Lt0UZ/R+35dSa0bP19UBk3tGYkyHUDhq5QGSiIjIGuSbIRvwR6FEhOwffkD6G2/K2jRhofCfMwcuPXvCki4l5WPv8iTcMrEZNFd8qGfYa4BOrQVWzgKyTks3xQp43+jd8JmHO8pqrAJWK9QY33w8JsVNkgqpXyk1twTz1p/Gb3vP1wp6AXodHu4ViZHtQqDTMOgRERH9GwY+IoLHqFHI/vY7GNLSoHBygvdDD8Fz/DgotZY98CLxQDrWfnUUhgoT1n1zFAMmNodCrJpNDU/2GWDVXODE8qqmvToHvOztjTNXhLI2vm3wbKdnEeURddVTN8UZvV/3nEeFUR70/Nwc8EjvKIxuHwKHK5aEEhER0dUx8BHZGVNZmbQnr2bNPHFfnt+sp1Cweg18n3oSGn9/i/ZRXGl+YM057FiYWNWWeCADF8/kISDK3aJ9oyuUFwFb3gO2fwQYK+sh5iiVeM/THYtcXWR31Tvo8UTbJ3B71O1QKuSzs5fyS/HJhtP4eff5Wqdu+rg6SDN6d3cI5YweERHRdWLgI7ITYogqXL9eKqvg/dBkuI8YIbvuNnCg9GVpRqMJm38+iaNbL1S1qR1UuOWBZgx7DYm4/TvhT2D1M0B+amUTgEUuznjP2xu5Cvns3O2Rt+OJdk/AQ+cha08Xg97GRPy0+xzKr6ijJ9bOm9IrEvd0ZNAjIiK6UQx8RHag7MwZXHrtdRRt3SrdTn/vfbjecgtUDezgoLLiCqz8/AhSjudUtTm7O2DIw3HwCXW1aN+ohoyTwPKZwNlNVU2JGjVe9vXDPmmfZXXYa6RvJC3fbO/fXv4UBWX4dFMiftiZXKtgunjq5kM9I6VTN3kYCxER0c1h4COyYcbCQmR+/Amyv/8eMBiq23NyULRtG9wGDUJDUZBdiqXzDiH7QlFVm3eIC4Y83BIuHlevyUYWWL65+W1g+zzAJB7HApQqFPjc3R1fu7vBUCPoiXX0xANZxjcbD62qei9oXnEFPt+SiK+2JqGkwih7ek9nLSb1iMDYzmFw0vKfJyIiorrAf1GJbJBgMiFv8WKkv/sujBmZsmuOrVvD75m5cGzWDA1FZkoBln50CEV55VVt4XHe6H9/U2h1fJtqEMs3jy8FVs4G8s5XNW911OFVv0CkKMQfJlSHvS6BXfBMx2cQ4lZdU6+43ICvtyXhs02JyC+t/uHD5YLpYtAb1zkczqyjR0REVKf4LyuRjSk5fASXXn0VJQcPytrVPj7wfXIm3IYOhULRcE66zEotxMJ39qOitHq2J653MLqOjIaSJ3JaXlYisGIWcHpNVVOGSok3fQOwSicut6wOb96O3pjVfhYGhA+oGmNlBiN+2nVOOnkzs7A60ItcdWpM6h6B8V3D4arTmPFFERER2Q8GPiIbIRiNuPjCi8j9/ffKGZnLNBp4jR8Hr8kPQeXijIbG3d8JARF6nDuaLd3uOiIKLfuGNKhQapcqSoCt7wNbP6g6fVPcafeHqyve9/FBgVAd9BRQYFTMKExrMw1u2sp9oQajCQv3p+L/1p2SaurV5KhR4f5u4ZjUPRJ6JwY9IiKi+sTAR2QjFCoVTMXFsrDn3KM7/GbPhkOjRmioVColBkxqjiUfHkTLvqGIautr6S7RyVXAiqeAnKSqpjMaNV4MDMN+ZQVQI+zFesbiuU7PoYVPC+m2ySRg2eE0vL/mJM5kVu/HFGlVSozpGCrV0hNLLRAREVH9UwjiWe1kNvn5+dDr9cjLy4NbAzshkaxfxcWLSBw0WFq+6Tf7abj06mU1M2WCSWBRdUvLSa7cp3diWVWTuAjzSx9/LHB1RIVQvezWUe2Iqa2mYkyTMVAr1VLZjw0n0vH2qpM4lpYve1rxr3VE22BM6xuNYA8ns74kIiIie88GnOEjskIVFy6gYM0aeI4bJ2sXC6aHfvkldM2aQunQ8GZQyksNWPftMbTuHwr/CL3sGsOeBRnKgO0fApvfBQzVyy8PODjghZAInDEWiWuGq9q7BXWTSi0EugRKt3ckZuHtVcex/1xuraceEheAx/s3RqSPvAg7ERERmQcDH5EVMZWVIevLL5H1+QIIpaVwiImBc6dOsvs4tWmNhqgor0wqu5B5vhAXTuVi+FNt4e7L2R6LS1wPLH8SyDpd1VSgUOCDkCj8T1UGiGHvb546Tzzd4WkMDB8ozRwfTsnDW6uOY8sp+Umwoj6xvlLQax4kD/ZERERkXgx8RFZAXC5XuG4dLr3xJipSUqraxdM4Gy1cCIWmYR98IdbWWzLvIAqzKw//KC2swKG159FzTIylu2a/8i9ULt88ukjWvE7vjdd8fZBukO+/GxY1DE+0ewJ6Bz2Ss4rw9qoTWBqfVutpOzTyxFMDYtAu3LPeXwIRERH9NwY+ogau9ORJpL/xBoq275C1K/V6uN99N6BUoiFLPZmDFZ8eRllx9UEfTbsFovvoaIv2y26ZjMDuBcD6V4DygqrmSyoVXouMw/qKLKBG2At1DcXznZ9Hh4AOyCwsw/Mrj+DHXedgMMm3f7cI0uPJATHoHu1tNftGiYiI7AEDH1EDZcjJQeZHHyHnl1/Fow+rLygUcB89Cj6PPQa1hwcaslN7LmHtt0dhMlSHg463R6DtwDCGAktI3Q8snQGkVddoFEfW/4Ia4wNHoEgMe39TK9SY0HwCJsVNgtGoxv+tPYXPNyeiqLx6L58owttZCnoDm/vz75SIiKgBYuAjamCEigrk/PwLMj7+GKa8PNk1xzZt4P/MXOiaNkVDX4J6YM057FiYWNUmFlHvMzYWMZ0CLNo3u1SaXzmjt2eBeBxqVfNpJz1eDIvBwdKLQI0c18K7hTSrF6GPxi+7z0m19K4smi6WVZjRrzFGtQuGWtWwZ5mJiIjsGQMfUQNTsH4DLr32mqxNHRAAvydnwnXQoAY/iyLWYdvy60kc2ZRa1abVqTDwoRYIieW+LrMSq+4c/QtY+TRQUL3frkwBLIhsjy9NWTCIYa9GqYXH2jyG0Y1HY2VCOiZ/uQlJWcWyp3R1UOOhXpGY0DUcTlr+E0JERNTQ8V9rogbGtX8/OLZsiZJDh6DQ6eD14ER43X8/lI6OaOjEmb3VXyQgcX96VZuzuwOGPtoSXkE8lt+sxKLp4umbp1bLmvd6h+JFHx8klV6StfcM7om5Hefi7CUN7py/E/Ep8tlljUqB+zqFY2qfKHg6a83yEoiIiOjmMfARWZAxPx9QqqByca5qUyiV8Js7B9nffQ/fJx6HJsB6lkCKs4+N4ryqAp9XkDNundoSLh46S3fNfhgrgO0fAZvektXUK1Bp8H5sF/xWfBYozahq99J54emOTyNE0xlP/+8ENp2sviYSJ5TvaBUklVgI8WQZDSIiImujEMQfyZPZ5OfnQ6/XIy8vD25ubpbuDlmIYDQi9/c/kPHBB9DfOQx+Tz4JW7J3eZJ0OufAyS3g4MifK5nNuZ3AkulAxjFZ86bQlnjJSYH0smxZ+/Do4RgV8RAWbLqIRQdTpRWgNfVs7IOnBsagWSBr6REREVlrNmDgMzMGPiratRuXXn8dZcePVzZoNIhcshja8HDYCvFtRdzLp+JhHuZRnA2sfR7Y/52sOdvJA280bo8VeX+Ptb+Fu4XjiTZzsSNBj6+3JaHcaKpVYmH2oFh0ifI2S/eJiIjsVb4ZsgF/9E5kJuUpKUh/620UrF5daxlkyeEjVhn4zhzMQHpSPjrdEVnrNalUDftwGZsg/rwu/ldg1VygOLO6GcCKJn3xhikdOTXCnkqhwtim4+FWMggzvk1GTnF1GQZRmJeTVGJhcPMA6VRVIiIisn4MfET1zFhYiKzPFyD7m28glMuPtne95Rb4PvUktMHBsDbxG85jy/9OSenC0U2Lln1CLN0l+5J5Clj2OHB2s6z5onckXgmLwabsI7L2WI9YDPSfhh82GnE287TsmpezFo/1i8Zd7UOhVXNWloiIyJYw8BHVE8FgQO7vvyPjw49gzJbvnXKIjYXf7Nlw7tgB1kYwCdi+8DQOrj1f1bZr8Rk07uAHRxee3ljvKkqBre8DW98DjNU/QDCptPij1W14ryABhTXCnlapxR2NxiM+oRVe3p4veyoHtRIPdGuEKb0i4arTmPVlEBERkXkw8BHVA8FkQtKYe1AaHy9rV3l4wGf6dLiPGA6FSgVrY6gwYt03x3B6X3XZBSc3rXQSJ8OeGSRtA5Y8BmSdkjWfa9QZL3h6YE/2bll7U484uBWOwZdLxVvysDesdRBmDohBkHvDL/dBREREN46Bj6geiKUVXHr2qAp8Co0GHmPvg/fkyVBZ6WE9pUUVWD4/Hmmnq+uzefg7SWHPzZuhoV6V5AJrngP2fytrNjp544fWt2Hepa0ozU6VFVBv7ng3tu5ujHKD/Kk6NvLEM0OaokUwT94kIiKyBwx8RHV0KqV4UElNXhMmIPd/v8GpbRv4PP64Ve7Tuyw/swRL5x1CzsXiqrbAaHcMeqgFdM5cClivh7IcXQSsmAUUyguln2x5J55XFeLIhbWy9nCn1kg5NQTr8+WF7iO8nTF7cBP0a+Jba6wSERGR7WLgI7oJppIS6TCWwk2bEfbjD7JlmkonJ0Qs/gsqvXXPpKQn52Ppx/Eoya/eLxbV1hd9xzeBWmN9y1KtRl4KsGwmcHKFrLncKxILWvTHF+dWwyBUT985qVyhyr0dh481E+eUq9o9nbWY3i8ad3cIhYZlMoiIiOwOAx/RDe7Ry1u8GBnvfwDDpcqZl9w//oDHqFGy+1l72Es9kYOln8TDUGasamvdPxSdh0VCwWP764fJCOz5Alj3ElBeWN2u1CC+/Vg8X5aI08nLZQ9xM7bBhZODIBhdq9rE0zbv79oID/eOhBsPZCEiIrJbDHxE16lo926kv/EmSo8elbVnf/0N3EeMkPbv2Qq9rxN0TmoUioFPAfQY3Rgtelnv0tQG71ICsHgakLpX1lwc3A7zotvjh6TlEKQqe5W00CMv9VYU5LeQ3f+2loFSPb0QTyezdZ2IiIgaJgY+omtUdvo00t97H4Xr18svqFTwuOsueE99xKbCnsjFw0E6lGXxhwfR8+4YRLTysXSXbLfUwua3gG3/B5hqnLKidcWuzvfjhew9SElaJnuIKb89stIGAabqUNc+3ANzhzRFqxB3c/aeiIiIGjAGPqL/UHHpEjI++gh5C/8ETCbZNZfeveH75Ew4RETAVnkFueC+lztDreV+vXohFk5fMh3ITpQ1FzQeiHcDQ/FH8p+ydoXRE0Wpd8BY1LiqLczLCbMHNcGAZn48kIWIiIhkGPiI/kXuwj9x8aWXIJSWytodmjSB36yn4NypE2xFWYkB238/hU7DImvV1GPYqwfF2cCaZ4EDP8jbXfyxuctEvJi6EunJNZYNCwqU53RGWfoAQHCQmlwd1JjWNxpju4TBQc2/IyIiIqqNgY/oXzhER8vCniYwED7TH4Pbrbfa1PLNwpxSqexCVmoRstOKcfv0Vgx59Vlq4cgfwMqngaIM2aW8NvfhLb0TFp/6TtZuKvNBadpwGEvCpdvieTl3dQjF4/0bw9ulMvwRERERXQ0DH9G/1NJzbNEcboMHoWjbdnhNeQged98NpYNtfcDOTCmUwl5Rbpl0++KZPBzelCqdxkl1LPc8sOwJ4NQqebt3DDZ2vh8vJf4PGTnVIVAQlCjP6oHyzL6AUHnSZucILzw3tCmaBLiZu/dERERkhRj4yO6JQa9o6zZkfPABAt9+q9Z+PL/Zs6FwcIDKzfY+YJ8/no2Vnx5GeWl12YUWPYPQsm+IRftlk6UWdn8OrHsZqCiqbldpkdtlKt5QF2HZkY9lDzGW+qM0bQRMpcFV+/TmDm6C/k25T4+IiIiuHQMf2bWSIwlIf+cdFO/cKd3OeP99BH/0kew+ah/bPJnyxM40rP/uOEym6mP+u9wZhVb9Qxgo6tLFI8DiR4EL++XtoZ2xrv09ePnol8gqzZLP6mX2lr7Et2hxn96jfaMwrks49+kRERHRdWPgI7stsZDx4UcoWL1a1l6wZq10zSEqCrY8o7lvRTJ2LT5T1aZUK9BvfFNEt/OzaN9siqEM2Pw2sPV9eakFBzfk9H4ar5clYcX+t2QPMZYGoPTCSJjKAiFm7rvah+KJW7hPj4iIiG4cAx/ZlfKUFGR+NA95S5bUKrHg2LJlZYkFGw57JqMJm34+iaNbL1S1OTipMXhKCwRGe1i0bzbl3K7KWb3ME/L2JrdhddyteDX+E2SXZlc1C4KqxqyeCp0iPPHcrc3QNND2lhETERGReTHwkV2ouJSOzE/nI/f3P4CKCtk1bUSEdPKma//+Nr2U0VhhworPDiP5SPXyQRdPBwyd2gqegc4W7ZvNKCsE1r8M7PpMjHHV7S7+yLrlRbyWvQerd78ie4ixJKhyr15ZAEI9nTBnMOvpERERUd1h4CObV7z/AM5NmAChrPIUyss0QUHwnjoV+qG3QqG2/f8VxGWbTm7V9fW8Q1xw69SWcNZzuWCdOL2usoB63jlZs9B6LFbF9sJrB/4POWU58lm9jH7SKZwuDg6YOigKE7pynx4RERHVLdv/lEt2T9e8GVRenjBcSJNuq3y84T1lCjxGjIBCKy8wbsvEGaOe98RUll9QAAMebA6tjm8BdVJAffUzwMEf5e0e4cgc8ApeSVuHdTtfkF0ylgRLs3pCuT9GtwvBzAEx8HFl8CYiIqK6x097ZFNMJSWASgVljSAn/trnkalIf+steE16EB5jxkDp6Ah7pFIpMXByC2m2T/w13aSjfwHLZgJF6dVtCiWEjlOwLKId3tj3JvLK86ouCSY1yjPFWb3uaB3qhReGNkPLEHfL9J2IiIjsgkIQj+wjs8nPz4der0deXh7cbLCumyWDXs4vvyLryy/hNfEBeI0fL7suGAzSfVSurrAXp/elQ6VWoFFL2ywrYVEFFysLqB9fKm/3bYqMAa/gpeRF2Hh+o+ySsSREOoHTUxuC2YNiMax1EJRK7tMjIiKyZ/lmyAac4SPbCHpffAFjVuVhJFmfL4D7iJFQuVQfRCLu0bOXsCf+DOfQuvPY9vtpqDVK3P54a/g30lu6W7ZB/PnYgR+A1XOB0uqZOyg1ELrPxJKgxnh917MorCiofohJjbKMW4C87niwaySm9omCq05jmf4TERGR3WHgI6tkKi6umtG7HPQuMxYUoGT/Prj06AF7IxZR3/bbKcRvSJFuGypMWPPVUdzzQkcouYTz5mSfBZZOB87IZ+4Q1A6XBryEF07+iK075Pv4jMVhKEkbge7hTfH8hKaI9HExb5+JiIjI7jHwkc0EPWg0cB8xHN4PPghNYCDsjaHcKIW7Mwczqtqc9VoMmtycYe9mmIyVZRbEcgsVxdXtGicIvZ/BIm9/vL75CZQYi6ouCSYNytIHwF/RD8/f1Rx9m/iyzAIRERFZBAMfWU/Q+/mXyqCXXV2wWmLnQU9UUliOZR/H49LZ/Ko2sbaeWHbB1VNn0b5ZtfRjwF9TgdS98vZGPXGx3zN4+tAC7Du1U3bJUBwOZIzG9O6d8EC3RtBpWGaBiIiILIeBj6yCITMT6e+9BxiNVW0KMeiNHAEvMegFBMBe5WUUY8lHh5CXXlLVFhTjjkGTW8DBiXvFboihHNj6PrD5bcBUUd3uoIdwyyv4xVGHt9dNRYVQcsWs3kAMDBmOOWOaIkBvnyfBEhERUcPCwEdWQRsaCv3ttyNv4UIGvRrEGb1lnxxCSUF1KGncwQ99xjaBSs1lnDckZR+weCqQflTeHnsrUnvNxKPbP8Cpgv2yS4aiCAQbx+PVUT3RoZGneftLRERE9C8Y+KhBqUhLQ9ZXX8O1b184d+oou+b90GQodQ4Men9Lis/EqgVHpINZLmszMAydbo/gfrEbUV4MbHgV2PmJOF1X3e7sA9Ogt/BJSQEWrHwQJkVZ1SXBpIUyZyjmdB6HMR3DoWKZBSIiImpgGPioQSg7c1YqrZC3eDFgMKDs1KlagU+c5fN/7jmL9bGhUWmVMBkry2iK+a7H3TFo3iPI0t2yTmc2AUumATlJ8vaWY3CywxRM2fQa0isSgBp5zlgUhYH+0/Dc6K5wd9KavctERERE14KBjyyq9OhRZH6+AAWrVlXWOPtb8c6dKDl0CI4tW1q0fw1ZSKwneo+NxaafT2LAA80QHudt6S5Zn5JcYM2zwP7v5O36UBiHvItnz53BktUTAGV51SXB6IAA00h8cPtDaBbE+oZERETUsDHwkUUKgxdt3Ybsr79G0fbtta6rvL3hNX4ctJGRFumfNYntFICQJp5w1jtYuivW5/gyYOnjQOHFGo0KoONkbIkcjZnbXkWx8iRQYyuksjQGT7R5Bve1a8lls0RERGQVGPjIbEzl5chfshTZ33wjLdm8kiYoCF4TH4D+zjuhdGCAqam0qAJHNqei7YAwKK7YJ8awd50K04EVTwEJf8rbvRujcOD7eOTgLuzbNhEKZYVsVq+jfgI+GD0JrjqefEpERETWg4GPzKZ4126kzZ1bq10bFQnvSZPgNngwFGoOySvlZ5Vg6bx45KQVoayoAl1HRFu6S9ZJXDIc/yuw8mmgJKe6XamG0HU6vnXqg/fXvwWTwxkoaszquRhb4L2+L6NzGGeciYiIyPrw0zWZjXO3rlK4Kz+dKN126tgRnhPGw6VHDyiULCFwNRnnCrD040MozqvcQ3Zw7XkENfbgfr3rlXseWDoDOL1G3h7QCme6voHJu9YhTfkoFA41au6ZdBge9gie6zUWSo5PIiIislIMfFTn+/OKd+9Bzk8/wW/OHGj8fKuuiXuevCZOlPbviUHPsVkzi/a1oUtOyMKqz4+goqy62HzLviEIa+5l0X5ZFZMJ2PcVsOZ5oLywul2tQ1n3p/FCbjP8teU1qJySah7AiUBtG8wf+DoiPAIt0WsiIiKiOsPAR3XCVFyMvMVLkPPjj1X787RhYfB9fIbsfu533CF90b87uu0CNv54AoLp75NLFUC3EdFS4KNrlJUILH4USN4maxbCumBN1Bw8vX8Vyt1mQeVkqLqmFJwwNe4JTGw9koeyEBERkU1g4KObUn7uHHJ++hm5CxfClJ8vu5bzyy/wnjwJSmdni/XPGmdIdy85i73Lq+vBqTRK9J/QFJFtqmdL6V8YDcDOj4ENrwGG0up2rSsudZqDKaf9cPzIm1C5J8tm9WLdOmHeLa/Az9nPEr0mIiIiqhcMfHTdBJMJRdu2I+eHH1C4ebOsft5ljm3bwvPee6DQsiD1tTIaTNjw/XGc2FVdJsDBWY0hU+IQEOVu0b5ZjUsJwF+PABcOyJoNEf0wz/lhfLpvDdTeC6ByqJ7V0yqcMafjHNzZeChn9YiIiMjmMPDRdcldtAhZn36G8qTqGajLFA4OcBt6KzzvuQe6Jk0s0j9rVVZiwIpPDyP1RPXpkW7eOgx9tBXc/Zws2jerYCgHtrwDbHkXMFWHOcHRA/ubzsJDR11R5PYeNL7nZA9r79sNb/Z8CT5OPhboNBEREVH9Y+Cj62LMzKwV9jSBgfAYczf0w4dD7eFhsb5ZM8XftfYu82vkhsFT4uDkxhnS/5Syr3JWL+OYrLkg8lY8UXg3NhzbAgff1VApq4Ogo8oVz3WegyERQzirR0RERDZNIYibhshs8vPzodfrkZeXBzc3NzRUhqwslCcnw6lNm1rtp3r1Bioq4NSpk7Rs06V3byhUKov11VYU5pThj7f2wifUFf0faAaNln+m/6q8GNjwKrDzE3GdcVWzydkPCwOmY/ZpJ2j8foPKST6r1yOoN17s+hy8HVnagoiIiGw/GzDw/a2iogILFizAl19+icTEyjpxERERGDduHKZMmQJtHe1Fa8iBTzAaUbRjJ3L/+B0Fa9dB7eWFqHVra4W53D8XwbFFczhERVmsr7aqILsUzu4OUCo56/SvkrYCf00Fcs7KmpNDh2HChduQot0Jrc8aKGrM6rlq9Hi281wMDB/IWT0iIiJqEBj4zOiee+7BTz/9hOHDh6Nv374wGo1YsmQJVq9ejVGjRuHXX3+12cBXlpiIvEWLpLIKhkuXZNeCP50P1169LNY3W3ViZxr8I92h93G0dFesS2k+sPZ5YO9XsuYK12C8rZmCLzKdoAv8HSrH87LrfUP74plOz3BWj4iIiBoUBj4zuXDhAoKCgnDHHXfgzz//rGoX/2h69OiBbdu2ISsrCx51sD+toQQ+Q04O8pcvR96iv1B6+PBV76Nyd4fvU0/B/c5hZu+fPZRdEA9jGf5kW+hcNJbulnU4uRpYOh3IT5U17/EdgftTBqLMYze03uKsXnWher3WHc90mosB4QM4q0dEREQNjjmyAQ9tEZfRFRRI3wMDA2Xt4gfEgIAAKJVK6HQ62AJxX176u++hYMMGaR9eLQoFnDt3gv7O4XC9pT+ULKtQb2UXci8VY913xzDk4ThLd61hK84GVj4NxMtn2QtdwjGjZCLWXXSGLvQrODimyK73D+uPuR3nwsvRy8wdJiIiImo4GPgAREZGSl9fffUVWrdujX79+klLOv/66y8sXLgQs2fPhqOjbSy9Uzo5oWDdOsBYPQsi0jZqBP0dd0B/21BoAgIs1j97K7vQdTj3Qf4jcfFBwp/A8ieB4szqZoUKfzkNx6zMARC8dsIpeK1sVs/DwQNz/57VIyIiIrJ3DHziH4JajcWLF0sHtDz44INV7RqNBh999JF0aMuNEmcIazKZqk8TrM/C6CX79qFg7Vr4zpwJhaZ6yaDaxwfO3bqiaNNmKPV6uA0eBPc77oAuLo5L3urxIJal8w4h+0KRrOyCOLPn6MoZ1KsquAgsewI4vlTWfNExGpPzx+OwyRG68C+humJWTwx5YhF1T52nmTtMRERE1DDZVODLzc3FBx98cM33nzZtGjw9Kz8YijN40dHRaN++Pfr06YPi4mJ8++23mDp1KpydnTF27Fg0ZFLIO3gIBatWIX/FChjS06V2565d4dKjh+y+3pMmwX3YnXDp05tLNutZxvkCLJt3CEV55VVtjVp6s+zCv83qHfwRWDUHKM2rajYqNViAEXgn5xYovbbDyVs+qycGPHH55i3ht1io40REREQNk00d2pKUlIRGjRpd8/1PnTqFqKgoXLx4Ec2bN8fEiRPxxhtvyGbjunbtioSEBCQnJze4Q1tM5eUo3rlTKqFQsH69VBT9SuIyzcA3Xr+p34duzLmELKz8/AgqyqqDSVzvYHQdGc2yC1eTkwwseQw4s0HWfFITi4cL78cZrRq6gN+gcpQf2iKWWZjdcTZn9YiIiMjq5PPQlusTHh4unYJ4vT7//HPpFM6RI0fK2sXDWkaMGIGdO3di//79UrkGSzOVlUlLNQvXrUPhps0wFVUvE5RRKODYtg2cOnQwdxcJwNFtF7DxxxMQTH+PRwXQbUQ0WvYNsXTXGh5xmfOeBcDaF4GK6vFcrtThrfKR+Kq0H9Rem+Hksx4KhXxWTyy1IB7OQkRERER2EPhuVGpq5YyBeFDLlQwGg+y7pQkVBqQ9PRvCP5yw6diyJVwHDoDbwIHQ+PtboosEoCi3rCrsqTRK9J/QFJFtfC3drYYn81RlAfXzO2XNexQt8HjJ/UjVmqALmQ+V7oLs+qDwQdKsnofu5mfdiYiIiGwZAx+Apk2bSt+/+eYbdKgxI1ZRUSEVYxcPdRFP7zQXY2ERinfuQOGWrfCd+QRUrq5V11QuznDq0lk6dEWi0cC5Uye49usH1z69pUNZyPLaDQ5HflYpkg5lYvDDcQiI1Fu6Sw2LsQLY/iGw8U3AWFbVXKxwwovl9+BXYzdovTfCyXujbFbPS+eFZzs9i75hlp9tJyIiIrIGDHwAJkyYgP/7v//D/PnzkZKSggEDBkiHtvzwww+Ij4/Hk08+CV/f+pudEWfrSg4fRvGuXSjauQvF+/dX1chz7tIFbgPkB1Hob70VKmdnuPTtC5eePaFycam3vtGNEU887XVPDIoGh8PN2zZKetSZtHjgr0eAi/Gy5nWmNphTfj8ydEVwCv0YKl1lvcLLBjcajNkdZsNd527mDhMRERFZL5s6tOVmpKen46WXXsKyZcukJZ5iSYZmzZpJZRrEw1zqqmTB5Y2ZF7dvhzohoSrgCcXFV72/fsRwBL7ySp383lQ/8jKKkZaYh9hOrF/4rwxlwKa3gG0fAKbqJdI5cMNz5WOxRGgHB5+10HhugUJR/bbk7egt7dXrG8pZPSIiIrIt+WY4tIWBz0J/qbujouGi+udj+RVOTnDu2BFugwdDP/RWs/aRrl3a6Vwsn38YpcUVGPxQCzRqySW1V5W8A1gyDcg8KWv+y9gFL1aMRZ5jFnQBf0DpID9p9vbI2/Fk+yehd+CSWCIiIrI9+Tyl046oVNA1bwbnDh3h3LkTHNu1Y428Bu7knotY9+0xmAyVPzNZ/WUC7n2pM5zdHSzdtYajNB9Y9yKw5wtZ80XBA3Mr7sc6oTkc/FbCyXOH7Lq/sz+e7/w8ugV1M3OHiYiIiGwLA5+lKBTQNW0Kp44d4dSxA5zateNePCshTorvW5GEXYvPVrUplAp0GxnNsFfTiRXA0seBAvkJmz8beuN1wxgUOV+As//7UGpzZddHx4zG9DbT4aLl/w9EREREN4uBz0Ki1q6BR2iopbtB18loMGHjD8dxfGf1gSJanQoDJ7VASFMW/pYUpgMrngIS/pQ1nzX5YY5hInYgAg4By+Dkvld2PcQ1BC92eRHt/dubucNEREREtouBz0JU7jxp0NqUFlVg5WeHkXqyekbK1VOHIVPj4BXI2SiI24EP/gSsmgOUVv8ZGQQlPjfeiv8z3AmDy2k4B7wHpbqg6rpSocR9Te7DI60fgaOaJ5oSERER1SUGPqJrPIlz6bx45F6qPk3VN8xVqrHnrOcyTmSfBZY8BpzdJGs+bArHrIpJOKb0hkPgH3DSH5Jdj9RH4qWuLyHOJ87MHSYiIiKyDwx8RP/h4pk8LPskHqWFlbURRRGtfdBvQlNotP980qpdMBqAXfOB9a8ChpKq5hJBi/cMI/CVcSAUbglw8f8eUBVVXVcr1Li/xf2YHDcZWhUPJyIiIiKqLwx8RP9B46CCyWCqut26fyg6D4uUDmqBvRdQX/wokHZQ1rzV2Ezaq3de5Qhd8E9Qux6VXW/i2USa1Yv1jDVzh4mIiIjsDwMf0X/wCnKRDmVZNj8e3UdFo1n3INi1ihJg05vAtg8BwVjVnCs441XDPfjN2ANq/T64BSyHSVE966dRavBwq4cxrtk46ddEREREVP8Y+IiugXgC532vdOZ+vaStwOJpQHairHmpsZNUQD1TY4Jb6DcQdCdRPScKtPRpiZe6vIQI9wizd5mIiIjInjHwEdUg7tPbveystGTzyv15dh32SnKBNc8B+7+VNacJnni2YgLWmlpB67UNet91MKKs6rpOpcO0NtMwJnYMVEo73+9IREREZAEMfER/y7lYhKUfxyM/owQl+eW4ZWIzKBR2vk9PdGwJsGwmUFhde1D0vaEf3jLchSKHXHiHfYYy1TlUL/AEOvh3wAudX0CIW4jZu0xERERElRj4iACcO5qFVQsSUF5ikG6f3peOiFY+iG7vB7uVlwIsfwo4sUzWnGgKwKyKB7EXkdAHboCLfiPKaizgdNW6Yma7mRgWNYyBmYiIiMjCGPjI7sVvSMHW305BMAlVbW0HhiGqrS/skskI7P4cWP8KUF5Y1VwhqDDfOBQfG+6A0fkc/MPnoch0SfbQW8JuweyOs+Ht6G2BjhMRERHRlRj4yG4ZjSZs/d8pHNmUWtWmVCvQ574miOnoD7t04WBlAfUrSi0cMEXh6YqJOKHwRnD0KuSpt6Goxqksvk6+eKbjM+gd2tv8fSYiIiKif8TAR3aptKgCqxYcQcrxnKo2R1cNBk+Jg3+EHnanrBDY8FplEXWhOsnlC47SPr2fjH3g4XcCAb4/I89Q/WcmGh0zGtPbTIeL1sUCHSciIiKif8PAR3Yn91Ixln0SL32vWWtv8MMt4OblCLtzYkXloSz5KbLmpcaOeKliLHIclGjcfCFSy/ehvHKLoyRCH4EXuryA1r6tzd9nIiIiIromDHxkV7LTirDw7X0oK65OLuFx3uh/f1NodXb2v0P+BWDFU5WncNaQInjjuYrxWG9qhaYxCYD2T6SWV4djtVKNB1s8iIktJkKr0lqg40RERER0rezsEy7ZO3dfR/iFu+Hc0WzpdutbQtHpjkgolQr7OpRlz5fAupeA8oKqZoOgxFfGQfjAMBwePmVoEf4jkgoTAIO8gLpYaiHKI8oyfSciIiKi68LAR3ZFqVLilgebY9F7+xHXOwRNugTArqTFA0unA6n7ZM2HTBGYUzERiZpgtO+wH4cLFyO/sDrpOamdMKPtDIyKGQWlQmmBjhMRERHRjWDgI5tmMgm1Zu8cHNUY+XQ7KfzZjfIiYOPrEHZ8AoVQXR69UNDhbcNofG/sj47NshCi+wQHCy7IHtoruBfmdpoLf2c7PbmUiIiIyIox8JHNyrlYhJWfH0HPMTEIjHKXXbOrsHd8ObBiFpB3DjWj70pje7xQMRZOfu7oEb0a+7I2AtVb9eDj6INZHWZJtfVYQJ2IiIjIOjHwkU1KOpyJNV8moLzUiJWfHcbI2e3h6qmDXclJqgx6J1fKmi8Inni+Yjx2ajqgd/dE7Mmbj31Z1QXWFVDgrti78GjrR+GqdbVAx4mIiIiorjDwkU0RBAH7VyVj519nAKGyraSgAmcPZSKudzDsgqEM2P4hhM3vQGEorWo2Cgp8axyAdw0j0a21BjGO32BD5lHZQ5t4NsFznZ9Dc+/mFug4EREREdU1Bj6yGRXlRmz47hhO7U2valOpleh9XyxiOtrJ/rPEDRCWzYQi+7Rs+eYBUxSeqbgf6pBYDIrZjjWpv8NUWl1g3VHtKM3o3R17t1R2gYiIiIhsAz/ZkU0oyC7F8vnxyDxfvTTR2d0Bg6e0gG+YG2xefhqwei5w5A9Z0MsRXPCm4S5schqIoX1ysT7jdaxKuSR7aJ+QPpjdcTYPZSEiIiKyQQx8ZPUunM6V9umJSzcv84/QY+Dk5nDWO8CmGQ3AngUwrXsFyorqsCv6xdALH2AMBncLQmvNL/g5eZPsuhjw5nSYg96hvc3caSIiIiIyFwY+smoJW1Kx+ZeTMBmF6n1oXQPQ864YqDQ2fhLnuV0wLn0cqvQjqPlKj5lCMbfifvg174KR0fvwy8l3UGqs3sunUqhwb5N78XCrh+GkcbJI14mIiIjIPBj4yGrlZ5Vgy/9OVYU9hVKBbiOj0aJXkG2XESjKgrD2eSgOfA9VjWaxpt57hpHY4zsCw7qV4c9zr2DrsSTZQ1t4t5AOZYn1jDV7t4mIiIjI/Bj4yGq5eTmi972xWPv1UeicNRgwqTmCYzxgs0xGYN/XMKx9GeqyXNmlJcZO+EQ7AcMGRCOq7Ee8f2SN7LpYXuGx1o9hROMRUClrxkQiIiIismUMfGTVxNM3S4sq0CjOG27ejrBZydtRvuQJaDOPyv6nTTQF4CXTBER3HoxbA3fgi6NTUGIokT10WNQwTG87HZ46T7N3m4iIiIgsi4GPrKa+XuL+DDRq6S2VWqipZZ8Q2Kz8Cyhf8Qy0x/6AtkZzqaDBPMMdSGx8P4Z1LMOXx+YgKV6+fFNctjm341y08m1l9m4TERERUcPAwEcNnqHciE2/nMTx7Wlo0TsYPUY3hs0zlMGwbR6EzW9Da5TP2C03dsDP7pMxckBTpGV+iWd3rZZdd9W44tE2j2JU41FcvklERERk5xj4qMEfzLLysyPIOFcg3T68IQV+4W42XUhdOLkKxYufhHNhsqz9pCkIH2gmouug29HTaSNeOTS+1vLN2yNvx4y2M+Dl6GXmXhMRERFRQ8TARw3W+WPZWP1FgrRH7zKfUFcEROlhk7ISkbdoJvTn18O5RnO+4Ih5wkg4dp6MO2Iz8OHBx5CcLw+DMR4xmNtpLlr7tjZ7t4mIiIio4WLgowa5X+/A6nPYuSgRQnV5PcR29kfPu2Og1trYMsWyQuSveQOO+z6FXqgOtyZBgd9MPXGs6QwM6u6Lr49/iK82b6u1fHNq66kYFTMKaiX/dyYiIiIiOX5CpAalvNSA9d8eQ+KBjKo2pUqB7qMbo1n3QNuqr2cyoXjvDzCueRFuFZmySwdNkfjd7zEMHdIXSek/4qH1v8AgGGT3uS3yNmn5prejt5k7TkRERETWgoGPGozsC0VY+flh5Fwsrmpz1msxcHIL+EfY1jLOssQtyF80Ez4Fx2XtGYIbvnYcj9ZDJyNOvR1P7hyDnLIc2X3ifOIwu8NsNPdubuZeExEREZG1YeCjBiE9OR9/vrsfhnJTVVtgtDtumdgMznoH2Apj5hmk/f4kgi+uhU+N9gpBhV+Vg6DuOxs9Iovxzt7pOJFzQvZYX0dfqZ7ekIghUCrkpSmIiIiIiK6GgY8aBK9gF3gHu+LimTzpdlyfYHQZHgWVyjaCjVCSg+Q/X0LQye8QDPnSzHVCWyS2ehq9esTgs8Mf4tU18jILWqUW45qNw8QWE+GkcTJzz4mIiIjImjHwUYMgBrsBDzaTZvk6D4tCVFtf2ASjAclrPob7rncQLuTLLh0zhWJr1OPoM3gojpz7EWNWzEGZsUx2n/5h/fF428cR7Bps5o4TERERkS1g4COLKMguhaunTtbm4qHDmBc72cysXsqexVCsfgZhFfISChmCHit9J6L9sEfgmr8G968bjuzSbNl9oj2iMav9LHQM6GjmXhMRERGRLWHgI7MyGk3YsTARCZtTMXxWW2kZZ022EPbST+5GzuI5iCncI2svEzRY7nInwoc9g2BtIp7cNR5J+Umy+7g7uGNqq6kY3ng4yywQERER0U3jJ0oy66ze6i+O4OKZyqWNKz8/glGz20PraBvDMCvlFFIXzkVc9ipcuSB1o6Y7HAa8hOgwLd7d9zz2Xtoru65RanBv03ulfXpuWjez9puIiIiIbJdtfNKmBi/5SBbWfnMUpYXVhcXFmnrFBeVWH/hyMy/i5O8voFXab4hTyA9kSVBEI6Pr84js0AYfH/oIy5Yvq/X4wY0GY1qbaQhyCTJjr4mIiIjIHlj3J21q8IwGE3YsSsShtedl7VHtfNH73lhoddY7BPML8hD/+xtomfQ1OihKgBo14c/BH6daPI6WA0dh9fFv8MRfL6DcVC57fBvfNniy/ZOsp0dERERE9cZ6P21Tg5ebXozVXyQg41xBVZtSpUC3kdFo3jNImuGzRsWlpdi18CM0O/kxuiFHFvSyoMfhqIfQ/LYpSEpeiGcXD0FBefXrF4W5hWFG2xnoE9LHav8MiIiIiMg6MPBRvTix6yI2/XQCFWXGqja9ryMGTGwOn1D5QS3WorTcgK1Lv0Wj+PfQGymya8WCAw6G3IeYYbOQnrkRo1YOR0ZJRq0DWaa0nIKRMSOlPXtERERERPWNgY/qnLiEc/9KeSmCmI7+6HF3Y6tcwllWYcCWlf9D4P530E9IlF2rEFSI97sD4cNfQG7REYzdNAHnCs7J7qNT6XBPk3vwQIsH4Kq1zrBLRERERNbJ+j59U4MX3twLB1afg2ASoHZQoefdjRHbKQDWprTCiPUrFyJw/7voJxyrdT3BvTf87ngVxepsTN71OE7knJBdVyvUUnmFyXGT4ePkY8aeExERERFVYuCjOhcQ5Y4OQxshcX+6tITT3c8J1qS43IDVq5bBf/+7GCwcqnX9lEt76Ie8iFJPHWYeeBv7Lu2TXVdAgUGNBkn19ELcQszYcyIiIiIiOQY+uinF+eUQBAHOegdZe5sBYWjdLxQqjfUUUi8sM2D5mjXw2/sO7oC8Tp7ojFMcnAY+jwI/T7x56BPs2r2r1n16BPfAtNbTEOMZY6ZeExERERH9MwY+umFn4zOx4ftj8ApywW3TWkGhrD5xUin+usbthiy/tAJLV6+B9/4PMQo7al1PcYyFtv9zyA8KxOuHPsHOAztr3ae1b2tMbzMdbfzamKnXRERERET/jYGPrpt48ubW30/h6JYL0u2U4zmI35CCln2ta/lidlE5lq9agYD4jzAGe2pdv6SLgLrfs8gMjcD8Q/Ox/fD2Wvdp6tUUj7R6BN2DurPEAhERERE1OAx8dF0unc3Hmq8TkJdeIqutZ03OZxdjxcolaHxiPu5VHKh1PdMhFOq+c3AprAk+if8U2xK21bpPE88meLjVw+gZ3JNBj4iIiIgaLAY+uiYmowl7VyRj7/Ik6fTNyzwDndFvQlP4hDT8cgPH0vKxesWfaJO0AJOUh2UF00VZulCoe85EUlgsvkj4GttWvlbrOWI9Y6Vaer1DejPoEREREVGDx8BH/yk3vRhrvz4qze7VFNcnGJ3viIRaq0JDJR4os+tMFjat+gM90r7BY6qjwBXnyGQ4RsChz1NI8PPHgiNf4eDJd2o9T4xHDKa0moI+IX0Y9IiIiIjIajDw0T8SZ/KObE7F9j8TYSgzVrU767XoO64pQpp6oqEymgSsSbiAQ2t+wIDcXzBLeQa4IpdmuTSGQ79Z2OvqiC8SvsLJoydrPU+0RzQebvkw+oT2gVJhPSeOEhERERGJGPjoH6Ul5mHzL/IQFNnGB73GxELnokFDLa2wcNcpZGz9BneW/omByku1ZvSy9c3g0P8pbNBU4KuEr3C+4Hyt52nh3QITW0xEr5BeDHpEREREZLUY+OgfBUa7I6ajP07sugiNToUedzWWbjfEJY3iQSy/bj4EhwNf426sgLciv1bQy/FqDUXfGfjTmImfjn6E9JL0Ws/TMaAjHmzxIDr4d2iQr5OIiIiI6How8NG/6jYqGgIEdLo9Eq6eOjS0/Xn7knPw54YdiEr8DlNUG+CsKKt1v5zgfsjvOhY/5idg0YFXUWKoPmH0sr6hfaUZvebezc3UeyIiIiKi+qcQxE/NZDb5+fnQ6/XIy8uDm5sbGgJxCBzdegFZqUXSLF5DV1phxIrDF7B701L0yP4Dtyj3QqWQD2ODQo2imOE43XoQvkvdiA3nN0jBtSaVQoUhEUNwf/P7EekeaeZXQURERET2Lt8M2YAzfHauILsUG74/hvPHcqTboc08Ed7CGw2RuGzzlx2nULz3Z4w0LscwZXKtg1jKVC6oaDMOm8Ob47uzi5Gw87laz+Osccbw6OG4p8k9CHQJNN8LICIiIiIyMwY+O2UyCTi8IQU7F5+RncC57ffTCGvmBYVS0WBO29x0Mh1LtuxDVPIvuF+1Hl6Kglr780p0fsjrOA5/ujnj9zNLkL53Sa3nCnQOlELendF3wkXrYr4XQURERERkIQx8digrtRDrvz+O9CR5Xb3gWA/0vi+2QYS9zMIy/G/PORzdvgIDSpfhLeUeaNTVwfSyAt+2ON76VvxaloJ1536HQTDUuk+cdxzGNhsr7dNTKznkiYiIiMh+8NOvHTGUG7FneRIOrj4nzfBdptWp0PnOKDTrHmjRkynF2bwtpzKwfFcC3E/9jtGKdXhYmVZr2aZRoUFu7G1YE94Ev17cjtMnv671XGIpBTHgjW06Fq18W5nvRRARERERNSAMfHYi5UQONv5wHHkZ8hMqI1r5oPvoxnDxcLBY35KzivDbnvM4vW8NBpSuwMvK3XBQVdS6X6nOByda3YFFTmosP78Bxcf31LqPp85T2p83svFIBLgEmOkVEBERERE1TAx8duDCqRz89f4BWZuTXoued8UgorWPRfpUUm7EiiNpWLnrMEJSluIu1QZEK1NrzeaJ0gLbYXVESywqPIPTl1Zd9fna+LbB6JjR6BfWD1qVtv5fABERERGRFWDgswMBUe5SEfULp3Kl2817BKHTsEg4OJr3r19cRrrrbDaW7j+LoiPLMMS0ER8rD0Gjqb03r1jjhl1N+2KxoxobM/bBkLqm1n0c1Y64NeJWKejFeMaY6VUQEREREVkPBj4bJNbVq7kXT/x173tjseqLI+gxurEUAM3Zl6Np+fjrQCpOH9iMXqVrMVO1Ax6KwlqzeeKuwvig1lgbFIXlhWeQnr8PkJ8rI2nu1RzDoodhUKNBcNW6mu21EBERERFZGxZet6HiiqVFFdi1+AxUGiW6jYj+zyBY3zXz/jqYij3796J5znrcodpWuWTzKs44eWFNo1ZYpijC2aILV72Pu4O7NJsnBr3GHg2/ODwRERER0X9h4XW6JoJJwPGdadi+MBGlhRVSWYXYTgHwDpbXmqvvsCcevrLiyEXsO3QQEZfW4FbVDkxVJgGa2vdNV2mxKrw1VjppEF94Dig+ddWTNrsEdsGwqGHoFdKLe/OIiIiIiK4TA5+Vu5SUj63/O4mLZ/JlAXD/qmTc8kCzev/9T10qkELenvjDaJy5FkNVO/GQMvGqIS9NpcIq/yhs1HvgQMlFmExpQGHt+8V6xmJwo8HSkk1/Z/96fw1ERERERLaKgc9KFeaUYeeiRJzYdVHWrlIr0XpAKNoOCKu3g1cOpeRi3dFLOB6/EzF529BftRfTlGeuGvLOatRY5RGIje4eSKjIEc/nBErkpSFEQS5BUsgbEjEEke6R9dJ3IiIiIiJ7w8BnZSrKjTiw+hwOrE6GodwkuxbazAvdR0fD3depTn/PvJIKqSD6xmMXUHBiCzqW78Qo5T6EKjNqhTyxet5+nQM26n2wzdUNZ41/T+FJYa92zbwB4QOkoNfSp6VFi74TEREREdkiBj4rcnpfOrb9fkqa3avJ3c8JXYZHIbyFV52EJvFwl1PphdhwPB2HEhLgdmELuikO4VnlYegVxbVGzSWVClsdddjk6oFdjhoUC3+XWbgc9moQl2j2De0rfYm181TKqxTeIyIiIiKiOsHAZ0UKskplYc/BSY32Qxqhec8gaSnnzUjJKcb201nYc+o8yhO3omXZPvRRHsZk8WTNK0ZJvlKBvTod9ugcsNNFj9OqGge9Xg57NYS7hUsF0fuF9kNTr6acySMiIiIiMhMGPisS1zsYR7akSsFPLJ7e4dZG0LlcZePcNUjLK8HepBzsPXkeRae3IbzoIDooj+N2RSIcFAbZyChSKKRlmnt0Ouxy1OGYVguhKrPVruqhVqjRxq8Nugd1R/fg7ojQRzDkERERERFZAANfA5SfWYKDa8+jy/BIqDXVSx7F+np9xzWBzkkDz0Dna34+o0nAiYsF2JecjeOnT8Nwbg8iSw6jg/IYBimSoFaYqkaC8PdBK4ccHBDvoJW+n9ZqYPqPwObj6COFOzHkdQroBBetvCQEERERERGZn00Hvs8++wybN2/Gvn37cOrUKZhMJml/2j85ceIEZs2ahU2bNqG8vBxt2rTBiy++iD59+pilv8X55di7IgkJm1NhMgpw9dKhdf9Q2X0Co9z/9TnE13c+uwSHU/NwMjkVpcl74ZhxCDGmU+irTMR9iuzKO6orw90FtQontI44odXg8N8hL0/13/vqnDXOaOfXDu3926NjQEfEeMRwFo+IiIiIqIFRCP+WgKxceHg4srKy0Lp1a5w9exYpKSn/GPgSExPRoUMHqNVqTJ8+Xap4v2DBAhw5cgQrVqxAv3796qRP+fn50nPn5eXBzc1NaispLJdm9A5vSEFFWfUeOAdnNe57pQscHK+ey8sMRpzJKMLJi7lISzqOkpTDcMg6jlBjMpooziFSmVZ130KFAkkaDU5pNTip1eC4VosTWi0KVNe290+n0knLNKWA598RTbyaQK206Z8XEBERERHVq6tlg7pm04EvKSkJoaGhUCqVuPXWW7Fs2bJ/DHyjRo3CH3/8Ic0GtmrVSmorLCxEs2bNoNPpcPz48TqZwar5l6pR6q4a9ESOblq0HxyOpt0CUWQw4lxWMZJTLyD7/AmUpp+CMucs9CXnEa1IQWNFChwV5dJhKmlqNdJUaiRr1EiSvjTSV6b6+k7DFOviiaUSLn819mwMjfLG9gsSEREREZFlAp9NT9GIM3zXoqioCIsXL0avXr2qwp7IxcUFEydOxHPPPYc9e/ZIM4B1ZffSMzizK69W0FOqBbgEXoKj8xGc3ZmC1I0X4YwMuKgz4acug0apRLZKhWwnFY65KrFBrcYFtScuqtUoVN7YSZ1eOi/EesZKX3E+cdKXt6N3Hb1SIiIiIiKyFJsOfNcqPj4eZWVl6Ny5c61rnTp1kr7XdeATZ/YctdUHrxiVJbjouw5nfTcgV1uCYqUSRW4KlFWFuH/fu3ctxCWYoa6hiPGMkfbciQFP/DXDHRERERGRbWLgEw8uuXBB+h4UFFTr2uW21NTUG3rugIAA2W2jsXJGr7S8WPpeoSrDEd8tOOq/DWWayjYYLoc8cflp7bp2/0apUEonZgY4ByDMLUz6EkNeqFsoAl0Ca++7qwDyK/Jv6LUREREREdHNLekU1ecuuwYf+HJzc/HBBx9c8/2nTZsGT0/P6/o9iosrg5aDg0Ota+L+vZr3uVkZGRnS92d/vKtOno+IiIiIiKxbQUGBtJfPbgOfWBrhWt17773XHficnJyk7+KyziuVlpbK7nO90tKqT8oU+fn5SeUhTp8+zTIGVK+io6Ol72JJEqL6xLFG5sKxRubCsUbmEhUVJc3uBQYG1tvvobaGg1fq+yDRy3/AV1u2ebntass9b4R4Yqj4VV8JnugycZyJ6uvEJ6LLONbIXDjWyFw41shcVH/Xv7485upD/T2zFWnRooW0nHPHjh21ru3cuVP63q5dOwv0jIiIiIiI6MYx8P1dfmHo0KHYuHEjDh06VNUu1uH74osvpGn9ujyhk4iIiIiIyBxsuvD6kiVLqgLcDz/8gBMnTuDll1+Wbru7u2Pq1KlV9xX31ImhTqPRYMaMGdIU/oIFC3D48GGpYPuAAQMs9jqIiIiIiIhuhE0HvvHjx+Pbb7+96rWwsDAkJSXJ2o4dO4ann34amzZtQnl5Odq0aYMXXngB/fr1M1OPiYiIiIiI6o5NBz4iIiIiIiJ7xj18RERERERENoqBj4iIiIiIyEYx8BEREREREdkoBj4iIiIiIiIbxcBHRERERERkoxj4iIiIiIiIbBQDHxERERERkY1i4CMiIiIiIrJRDHxEREREREQ2ioGPiIiIiIjIRjHw1QGTyYT3338fsbGx0Ol0CAkJwRNPPIGioiKzPJ7sx82MlZMnT+K5555Dp06d4OPjA1dXV7Rq1QqvvvoqxxrV6/tScXExIiIioFAoMHXq1HrpL9n3WMvOzsbMmTMRFRUlPYf4Hte7d29s2bKlXvtO9jXWCgsL8dprr6FFixbSv6He3t7o0qULvvnmGwiCUO/9J+vx+uuvY+TIkVX/9oWHh9/Q83z33Xdo3bo1HB0d4efnh4kTJyIjI+P6n0igmzZt2jTx/3Jh2LBhwueffy7MmDFDUKvVQu/evQWj0Vjvjyf7cTNjZdasWYKLi4swZswY4cMPPxTmz58vjBo1Snq+uLg4obi42Gyvgxq+unxfeuKJJ6SxJz7fI488Um99Jvsca0lJSUJ4eLjg7e0tvc99+eWXwnvvvSeMHz9e+Pnnn83yGsj2x5p4vVu3boJSqRQmTJggfPbZZ8L7778vdOjQQXrOp556ymyvgxo+AIKnp6fQr18/wcPDQwgLC7vu5xDfx8Tn6dmzpzTenn32WcHZ2Vlo2rSpUFhYeH39ue7fnWSOHDkiKBQK4c4775S1ix+oxb+kH3/8sV4fT/bjZsfKnj17hNzc3Frtc+fOlR7/0Ucf1XmfyTrV5fvSvn37BJVKJbz77rsMfFQvY038EB4cHCxcuHChHntK9j7Wtm/fLt1v+vTpsvaysjKhUaNGgl6vr5d+k3VKTEys+nWzZs2uO/BlZGQITk5OQvv27QWDwVDVvnjxYmkcvvrqq9f1fFzSeZN+/vlnaRp/+vTpsvYHH3wQTk5O+OGHH+r18WQ/bnastGvXDnq9vlb76NGjpe9Hjhyp4x6Ttaqr9yWj0Sg9ZuDAgbjzzjvrqbdkz2Nt8+bN2Lp1K5566ikEBASgoqJCWkJMVNdjLT8/X/oeGBgoa9dqtdLSTmdn53roNVmriIiIm3r8okWLpPeyRx99FCqVqqp96NCh0nNfbz5g4LtJe/bsgVKpRIcOHWTt4tpwcX+UeL0+H0/2o77GSkpKivRdXBtOVJdjTdwrc/z4ccybN6+eekr2PtaWL18ufQ8NDZU+CIn7XMQP3o0bN+YPTKlOx5r4OHd3d7z11lv47bffcO7cOen9bfbs2di3bx9eeOGFen4FZE/2/D0eO3fuXOuaeBaDOPbEPaXXioHvJl24cEH6yY6Dg0Ota0FBQcjMzER5eXm9PZ7sR32MFXEG5uWXX4ZarcaYMWPqsLdk72Pt7NmzeP7556WDgm50szrZvpsdaydOnKiapREPbvn222/x1VdfSbMu9913H77++ut67T/Zz1jz8PDA4sWL4enpiVGjRiEsLAxNmjTBxx9/jD/++EMag0R1OV4vj80riW3ibPXl+1wLdZ31zE6J061Xe/O4/FOjy/cR//Gpj8eT/aiPsSIubdmxY4d06lhMTEyd9ZWsW12MtYceekhadvL444/XWz/J+t3sWCsoKJC+iycmbtiwoep+d9xxhzT+5syZg3HjxkkzO2Tf6uJ9zcXFBc2bN8dtt90mnc4p/pBBDHziD0z/+usv9O/fv976T/al+O+l6VcbszXH67XiO+BNEtd9l5WVXfVaaWlp1X3q6/FkP+p6rDz77LPSUrtJkyZJS1KI6mqsiUvp1qxZg/nz50Oj0dRbP8n63exYE5dwiu6++27ZB3VxNkb8UH7x4sWqWUCybzc71g4fPiyFPDHUvf322xg2bBgeeOABaQ+pv7+/NMMnrpohqguXx+LVxuyNfOZj4LtJ4uZdcRnA1f5CUlNTpeUD//bTopt9PNmPuhwr4l6DV155BRMmTMCnn35aD70lex1r4mPEWb3BgwdLH4JOnz4tfSUnJ0vX8/LypNu5ubn1/jrI9t/XgoODpe/iWLuSeIiLKCcnp077TPY51sQ9yeIHbbG2Wk3ih+4hQ4ZI73FJSUn10neyP4F/Hw4kjs0riW1ibb8rDxD6Nwx8N6l9+/ZSIc/du3fL2sU3hYMHD0onI9bn48l+1NVYEcPeiy++KC1z+uKLL6Q3DaK6GmslJSVSUdhly5YhOjq66qtXr15Vs3/ibXHsEd3s+9rlAzguHz5V0+U2X1/fOu0z2edYu/zB+2qzeAaDQfadqC7Gq0jcdnOlnTt3SttwxCXG14qB7yaJR9qLH5g/+OADWfuCBQuktbX33HNPVVtiYqJ0qs6NPp7s282ONdFLL70khT3xMAPxYAPua6G6HmviCYniCXZXfn3yySfSdbFEg3hbXG5HdLPva+JePXH/nviDhJon1qWlpUnHmoundUZFRZnhlZCtj7WmTZtK37/55htZu7haQdy/Jy4j5lijG3H5xFexrMxlt99+u7RkXdx6U/OHDEuWLMGZM2euPx9cV9U+uqqpU6dKRRCHDRsmLFiwQHj88ccFtVot9OzZUzAajVX3E4suXu2P/FofT3QzY23evHlSW2hoqPDtt98K33//vexr9erVFnhFZKvva1c6e/YsC69TvYy1zz77TGoXixu/++67wuuvvy69z2k0GmHVqlVmfjVkq2MtKSlJ8PT0lIq333vvvcL8+fOl4tfh4eHSfT/++GMLvCJqqL777jvh5Zdflr58fX0Fd3f3qtvitZrE8SeOIfHfyZreeecdqb1Xr17S+9xzzz0nODs7C7GxsUJBQcF19YeBrw4YDAbpL6Vx48aCVqsVAgMDhRkzZtT6y/inf6yu9fFENzPWxo0bJ7X905f4hkNUV+9rV2Lgo/oca3/88YfQsWNHwcnJSXBxcRH69+8vbN261UyvgOxlrJ0+fVoYO3asEBQUJAVFV1dXoXv37tL4I7paiLuWz1v/FPhEX3/9tRAXFyc4ODgIPj4+woQJE4RLly4J10sh/ueG5h+JiIiIiIioQeMGHiIiIiIiIhvFwEdERERERGSjGPiIiIiIiIhsFAMfERERERGRjWLgIyIiIiIislEMfERERERERDaKgY+IiIiIiMhGMfARERERERHZKAY+IiIiIiIiG8XAR0REREREZKMY+IiIiIiIiGwUAx8REREREZGNYuAjIiIiIiKyUQx8REREZlJWVoZ3330XcXFxcHR0RGhoKGbPng2j0YicnBzo9Xo89thjlu4mERHZEIUgCIKlO0FERGTrMjMzMXDgQOzfvx+33XYbGjdujEWLFuHUqVP4/PPPcf78eSkMnjlzBn5+fpbuLhER2QgGPiIiIjPo06cPNmzYgM8++wyTJk2S2lJSUhAeHi4Fwa1bt2Ly5Ml48803Ld1VIiKyIQx8RERE9WzlypUYNGgQ+vXrhzVr1siuBQcHIzU1FW5ubjh79iw8PT0t1k8iIrI93MNHRERUz77//nvp+9X25+l0Oun7jBkzGPaIiKjOcYaPiIionomzeOIevtzc3KqAd1lUVJR0YIs4uyfO8hEREdUlzvARERHVo6KiImnJZqNGjWqFvYSEBCQnJ6Nnz54Me0REVC8Y+IiIiOpRSUmJ9F2prP1P7rRp02AwGKBWqy3QMyIisgcMfERERPVI3Jfn4uKCEydO4OjRo1XtYgmG9evXS78Wl3oSERHVBwY+IiKieiTO7I0bN04qrt67d2888sgjGDZsGGbOnIm7774bHTp0wNq1a6XZviNHjli6u0REZGN4aAsREVE9Ky0txTPPPINffvkFly5dkmb9JkyYgFdffRXbtm3D+PHjpUNb9uzZg3bt2lm6u0REZEMY+IiIiIiIiGwUl3QSERERERHZKAY+IiIiIiIiG8XAR0REREREZKMY+IiIiIiIiGwUAx8REREREZGNYuAjIiIiIiKyUQx8RERERERENoqBj4iIiIiIyEYx8BEREREREdkoBj4iIiIiIiIbxcBHRERERERkoxj4iIiIiIiIbBQDHxEREREREWzT/wNKuy8crnHgGwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10, 6])\n",
    "states = [\"Hangover\", \"Sleep\", \"More Sleep\", \"Visit Lecture\", \"Study\", \"Pass Exam\"]\n",
    "alphas = alphas.flatten()\n",
    "for state, vnalp in zip(states, v_n_alpha):\n",
    "    ls = '--' if state in ['Visit Lecture', 'Study'] else '-'\n",
    "    plt.plot(alphas, vnalp, ls=ls, label=r\"$x=${}\".format(state))\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel(r\"$\\alpha$\")\n",
    "plt.ylabel(r\"$v_\\pi(x)$\")\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([-10, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-af98cbff882e9d6f",
     "locked": false,
     "points": 0,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "source": [
    "If you are rather lazy (low $\\alpha$), there is a good chance a studying session will end in just going to sleep. Hence, your chance (in general) to pass is lower if you tend to be lazy (due to lower $v_\\pi(x)$), but you should at least visit the lecture in order to go to the exam.\n",
    "\n",
    "If you are more productive (high $\\alpha$), studying raises your chances for passing significantly.\n",
    "\n",
    "Result: In order to be as lazy as possible, please visit the lecture :)"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Create Assignment",
  "kernelspec": {
   "display_name": "RL_course_py12",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
